Social & Information Networks (CSE3021) J Component

Analysis of International Air Traffic to and from India

Team Members:

1. S Arun (19BCE1106)
2. Shreya Thiagarajan (19BCE1139)
3. Vineeth Victor (19BCE1792)
4. T Rahul Reddy (19BCE1778)

Imporing Dependencies

import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import os
import operator
import warnings
import networkx_old as nxo
import numpy as np 
import pandas as pd
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import Normalize, LinearSegmentedColormap, PowerNorm
from mpl_toolkits.mplot3d import Axes3D
from gcmap import GCMapper, Gradient
from subprocess import check_output
warnings.filterwarnings('ignore')

Importing Airport & Route Dataset

G_df = pd.read_csv('routes.csv')
cols_list=["City","Country","IATA"]
airport_df = pd.read_csv('airports.csv',usecols=cols_list)

G_df.head(10) #route dataframe

	Airline	Airline ID	Source airport	Source airport ID	Destination airport	Destination airport ID	Codeshare	Stops	Equipment
0	2B	410	AER	2965	KZN	2990	NaN	0	CR2
1	2B	410	ASF	2966	KZN	2990	NaN	0	CR2
2	2B	410	ASF	2966	MRV	2962	NaN	0	CR2
3	2B	410	CEK	2968	KZN	2990	NaN	0	CR2
4	2B	410	CEK	2968	OVB	4078	NaN	0	CR2
5	2B	410	DME	4029	KZN	2990	NaN	0	CR2
6	2B	410	DME	4029	NBC	6969	NaN	0	CR2
7	2B	410	DME	4029	TGK	\N	NaN	0	CR2
8	2B	410	DME	4029	UUA	6160	NaN	0	CR2
9	2B	410	EGO	6156	KGD	2952	NaN	0	CR2

airport_df.head(10) #aiport dataframe

	City	Country	IATA
0	Goroka	Papua New Guinea	GKA
1	Madang	Papua New Guinea	MAG
2	Mount Hagen	Papua New Guinea	HGU
3	Nadzab	Papua New Guinea	LAE
4	Port Moresby	Papua New Guinea	POM
5	Wewak	Papua New Guinea	WWK
6	Narssarssuaq	Greenland	UAK
7	Godthaab	Greenland	GOH
8	Sondrestrom	Greenland	SFJ
9	Thule	Greenland	THU

#filtering out the indian aiports along with the IATA aiport code from the global aiports dataframe
Ind = airport_df[airport_df['Country'] == 'India']
Ind_cities = Ind['City'].unique()
Ind_Iata = Ind['IATA'].unique()
print(Ind_cities)
print(Ind_Iata)

['Ahmedabad' 'Akola' 'Aurangabad' 'Mumbai' 'Bilaspur' 'Bhuj' 'Belgaum'
 'Baroda' 'Bhopal' 'Bhaunagar' 'Daman' 'Deesa' 'Guna' 'Goa' 'Indore'
 'Jabalpur' 'Jamnagar' 'Kandla' 'Khajuraho' 'Kolhapur' 'Keshod' 'Nagpur'
 'Nasik Road' 'Pune' 'Porbandar' 'Rajkot' 'Raipur' 'Sholapur' 'Surat'
 'Udaipur' 'Along' 'Agartala' 'Aizwal' 'Baghdogra' 'Bokaro' 'Bhubaneswar'
 'Kolkata' 'Cooch-behar' 'Dhanbad' 'Gaya' 'Hirakud' 'Imphal' 'Jharsuguda'
 'Jamshedpur' 'Jorhat' 'Kailashahar' 'Silchar' 'Lilabari' 'Mohanbari'
 'Mazuffarpur' 'Nawapara' 'Panagarh' 'Patina' 'Purnea' 'Ranchi' 'Rourkela'
 'Utkela' 'Vishakhapatnam' 'Zero' 'Agra' 'Allahabad' 'Amritsar' 'Bikaner'
 'Bakshi Ka Talab' 'Varanasi' 'Kulu' 'Bhatinda' 'Bhiwani' 'Bareilly'
 'Chandigarh' 'Kanpur' 'Delhi' 'Dehra Dun' 'Gwalior' 'Hissar' 'Jhansi'
 'Jodhpur' 'Jaipur' 'Jaisalmer' 'Jammu' 'Kota' 'Ludhiaha' 'Leh' 'Lucknow'
 'Pathankot' 'Patiala' 'Nainital' 'Raibarelli' 'Saharanpur' 'Srinagar'
 'Satna' 'Agatti Island' 'Bangalore' 'Bellary' 'Bidar' 'Vijayawada'
 'Coimbatore' 'Kochi' 'Calicut' 'Cuddapah' 'Carnicobar' 'Dundigul'
 'Hyderabad' 'Madurai' 'Mangalore' 'Madras' 'Nagarjunsagar' 'Port Blair'
 'Pendicherry' 'Rajahmundry' 'Salem' 'Tanjore' 'Tirupeti'
 'Tiruchirappalli' 'Trivandrum' 'Tambaram' 'Diu' 'Hubli' 'Shillong'
 'Guwahati' 'Dimapur' 'Tezpur' 'Gorakhpur' 'Kangra' 'Nanded' 'Shimla'
 'Mysore' 'Nagaur' 'Pasighat' nan 'Durgapur' 'Puttaparthi' 'Tezu'
 'Adampur' 'Toranagallu' 'Shirdi' 'Jeypore' 'Ajmer' 'Kannur' 'Bengaluru']
['AMD' 'AKD' 'IXU' 'BOM' 'PAB' 'BHJ' 'IXG' 'BDQ' 'BHO' 'BHU' 'NMB' '\\N'
 'GUX' 'GOI' 'IDR' 'JLR' 'JGA' 'IXY' 'HJR' 'KLH' 'IXK' 'NAG' 'ISK' 'PNQ'
 'PBD' 'RAJ' 'RPR' 'SSE' 'STV' 'UDR' 'IXV' 'IXA' 'AJL' 'IXB' 'BBI' 'CCU'
 'COH' 'DBD' 'GAY' 'IMF' 'IXW' 'JRH' 'IXH' 'IXS' 'IXI' 'DIB' 'MZU' 'PAT'
 'IXR' 'RRK' 'VTZ' 'ZER' 'AGR' 'IXD' 'ATQ' 'BKB' 'VNS' 'KUU' 'BUP' 'BEK'
 'IXC' 'KNU' 'DED' 'DEL' 'GWL' 'HSS' 'JDH' 'JAI' 'JSA' 'IXJ' 'KTU' 'LUH'
 'IXL' 'LKO' 'IXP' 'PGH' 'SXR' 'TNI' 'AGX' 'BLR' 'BEP' 'VGA' 'CJB' 'COK'
 'CCJ' 'CDP' 'CBD' 'BPM' 'IXM' 'IXE' 'MAA' 'IXZ' 'PNY' 'RJA' 'SXV' 'TJV'
 'TIR' 'TRZ' 'TRV' 'DIU' 'HBX' 'SHL' 'GAU' 'DMU' 'TEZ' 'GOP' 'DHM' 'NDC'
 'SLV' 'MYQ' 'IXT' 'RTC' 'RDP' 'PUT' 'HYD' 'TEI' 'AIP' 'VDY' 'SAG' 'PYB'
 'KQH' 'CNN']

Visualisation of the Air Traffic Network to India

G_draw = nx.from_pandas_edgelist(G_df[G_df['Destination airport'].isin(Ind_Iata)], 'Source airport', 'Destination airport')
G_draw = nx.DiGraph(G_draw)
fig = plt.figure(figsize=(20,20))
nx.draw_networkx(G_draw,pos = nx.spring_layout(G_draw),with_labels=True)
fig.savefig('to_indian_aiports.svg') 

Visualisation of the Air Traffic Network from India

G_draw = nx.from_pandas_edgelist(G_df[G_df['Source airport'].isin(Ind_Iata)], 'Source airport', 'Destination airport')
G_draw = nx.DiGraph(G_draw)
fig = plt.figure(figsize=(20,20))
nx.draw_networkx(G_draw,pos = nx.spring_layout(G_draw),with_labels=True)
fig.savefig('from_indian_aiports.svg') 

#prints out the structural information (like no of nodes and edges) about the air route graph
G = nx.from_pandas_edgelist(G_df, 'Source airport', 'Destination airport',create_using=nx.DiGraph())
print(nx.info(G))

DiGraph with 3425 nodes and 37595 edges

Connectedness

Checking connectedness of airports

#this cell checks if every aiport is connected to every other airport
nx.is_strongly_connected(G), nx.is_connected(G.to_undirected())

(False, False)

Weakly and Strongly Connected Components

wccs = nx.weakly_connected_components(G)
x=len(max(wccs, key=len))
print("No of nodes in the largest weakly connected sub-graph = ",x)
print("Percentage of weakly connected components in the whole graph = ",(x/len(G.nodes()))*100)
#we can infer that 99% sub-graphs in the whole graph are weakly connected

No of nodes in the largest weakly connected sub-graph =  3397
Percentage of weakly connected components in the whole graph =  99.18248175182481

sccs = nx.strongly_connected_components(G)
x=len(max(sccs, key=len))
print("No of nodes in the largest strongly connected sub-graph = ",x)
print("Percentage of strongly connected components in the whole graph = ",(x/len(G.nodes()))*100)
#we can infer that 97% sub-graphs in the whole graph are strongly connected 

No of nodes in the largest strongly connected sub-graph =  3354
Percentage of strongly connected components in the whole graph =  97.92700729927007

Computing Average Shortest Path Length

• Average shortest path length is only calculated for graphs that have weakly connected components

Average Shortest Path Length for the largest strongly connected sub-graph

scc_subs = nxo.strongly_connected_component_subgraphs(G)
#fetching the largest strongly connected subgraph
G_sc = max(scc_subs, key=len)
shortest_sc=nxo.average_shortest_path_length(G_sc)
shortest_sc

4.125979351521906

print("No of edges per node in strongly connected sub-graphs = ",len(G_sc.edges())/len(G_sc.nodes()))

No of edges per node in strongly connected sub-graphs =  11.178294573643411

Average Shortest Path Length for the largest weakly connected sub-graph

wcc_subs = nxo.weakly_connected_component_subgraphs(G)
#fetching the largest weakly connected subgraph
G_wc = max(wcc_subs, key=len)
shortest_wc=nxo.average_shortest_path_length(G_wc)
shortest_wc

4.093938981010404

print("No of edges per node in weakly connected sub-graphs = ",len(G_wc.edges())/len(G_wc.nodes()))

No of edges per node in weakly connected sub-graphs =  11.052987930526935

From the above analysis, we can infer that the average shortest path length is less for the weakly connected subgraphs, even though the no of edges per node is higher in the strongly connected subgraphs.

Density of a network

print("Density of whole graph: ",nx.density(G))
print("Density of strongly connected sub-graph: ",nx.density(G_sc))

Density of whole graph:  0.003205795074698138
Density of strongly connected sub-graph:  0.003333818840931527

Visualising the Degree of Nodes in the whole graph

degrees = dict(G.degree())
degree_values = sorted(set(degrees.values()))
histogram = [list(degrees.values()).count(i)/float(nx.number_of_nodes(G_sc)) for i in degree_values]
plt.ylabel("Degree")
plt.xlabel("No of nodes")
plt.title("Degree of Nodes")
plt.plot(histogram)

[<matplotlib.lines.Line2D at 0x1c2cadcd4f0>]

Diameter & Radius

Note: Diameter and Radius of weakly connected component will be infinity

diameter=nx.diameter(G_sc)
print("Diameter of Strongly Connected Component: ",diameter)

Diameter of Strongly Connected Component:  14

radius=nx.radius(G_sc)
print("Radius of Strongly Connected Component: ",radius)

Radius of Strongly Connected Component:  7

Diameter represents the greatest possibe no of airports between any two aiports, we can infer that a max of 14 airports are between any two airports in the strongly connected sub-graph

Radius represents an airport from which every other airport is at a minimum no of airports apart, we can infer that every airport is away from a minimum of 7 airports to every other airports

Periphery and Center

• Set of nodes which have eccentricity equal to diameter
• Set of nodes eccentricity equal to the radius

per=nx.periphery(G_sc)
print("Nodes which have eccentricity equal to diameter: ",per)

Nodes which have eccentricity equal to diameter:  ['YZG', 'XEQ', 'KCG', 'YPO']

airport_df.loc[airport_df['IATA'].isin(per)]

	City	Country	IATA
4228	Peawanuck	Canada	YPO
4241	Salluit	Canada	YZG

cen=nx.center(G_sc)
print("Nodes which have eccentricity equal to radius: ",cen)

Nodes which have eccentricity equal to radius:  ['CPH', 'KEF']

airport_df.loc[airport_df['IATA'].isin(cen)]

	City	Country	IATA
15	Keflavik	Iceland	KEF
601	Copenhagen	Denmark	CPH

Analysing the Strongly connected Component

Finding out the nodes which are connected to most of the other nodes where the shortest path of length = diameter of strongly connected component

max_count = -1
result_node = None
for node in per:
    count = 0
    sp = nx.shortest_path_length(G_sc, node)
    for key, value in sp.items():
        if value == diameter:
            count += 1        
    if count > max_count:
        result_node = node
        max_count = count

result_node, max_count

('XEQ', 5)

airport_df.loc[airport_df['IATA'] == result_node]

	City	Country	IATA

Finding out the nodes which are connected to most of the other nodes where the shortest path of length = radius of strongly connected component

d = radius
max_count = -1
result_node = None
for node in cen:
    count = 0
    sp = nx.shortest_path_length(G_sc, node)
    for key, value in sp.items():
        if value == radius:
            count += 1        
    if count > max_count:
        result_node = node
        max_count = count

result_node, max_count

('KEF', 8)

airport_df.loc[airport_df['IATA'] == result_node]

	City	Country	IATA
15	Keflavik	Iceland	KEF

Transitivity and Average Clustering Coefficient of Strongly Connected Component

print("Transitivity: ",nx.transitivity(G_sc))
print("Average Clustering Coefficient: ",nx.average_clustering(G_sc))

Transitivity:  0.24878205149750107
Average Clustering Coefficient:  0.47733508870492014

Transitivity and Average Clustering Coefficient of Weakly Connected Component

print("Transitivity: ",nx.transitivity(G_wc))
print("Average Clustering Coefficient: ",nx.average_clustering(G_wc))

Transitivity:  0.24864166946590527
Average Clustering Coefficient:  0.4708160157290989

In-degree & Out-degree

in_deg=nx.in_degree_centrality(G_sc)

out_deg=nx.out_degree_centrality(G_sc)

Top 5 airports with most incoming flights

top5=sorted(in_deg.items(), key=operator.itemgetter(1),reverse=True)[:5]
print("In-Degree Values:\n",top5)
l=[]
for i,j in top5:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

In-Degree Values:
 [('FRA', 0.0709812108559499), ('CDG', 0.06919176856546377), ('AMS', 0.06889352818371608), ('IST', 0.06859528780196839), ('ATL', 0.06441992245750075)]

	City	Country	IATA
336	Frankfurt	Germany	FRA
574	Amsterdam	Netherlands	AMS
1346	Paris	France	CDG
3482	Atlanta	United States	ATL
7629	Istanbul	Turkey	IST

Top 15 Indian airports with most incoming flights

all_in_deg=nx.in_degree_centrality(G)
all_out_deg=nx.out_degree_centrality(G)

India_in_deg = {}
for x in all_in_deg.keys():
    if x in Ind_Iata:
        India_in_deg[x] = all_in_deg[x]
top = sorted(India_in_deg.items(), key=operator.itemgetter(1),reverse=True)
indian_in_deg_df = pd.DataFrame(top, columns = ["IATA", "In Degree"])
indian_in_deg_df.head(15)

	IATA	In Degree
0	DEL	0.028329
1	BOM	0.023949
2	BLR	0.011974
3	MAA	0.011098
4	CCU	0.010806
5	HYD	0.009346
6	COK	0.006425
7	AMD	0.005257
8	TRV	0.004965
9	CCJ	0.004673
10	PNQ	0.003797
11	GOI	0.003505
12	GAU	0.003505
13	JAI	0.002629
14	VTZ	0.002629

Top 5 airports with least incoming flights

bot5=sorted(in_deg.items(), key=operator.itemgetter(1))[:5]
print("In-Degree Values:\n",bot5)
l=[]
for i,j in bot5:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

In-Degree Values:
 [('TGK', 0.00029824038174768865), ('KCK', 0.00029824038174768865), ('ODO', 0.00029824038174768865), ('UKX', 0.00029824038174768865), ('AYP', 0.00029824038174768865)]

	City	Country	IATA
2648	Ayacucho	Peru	AYP
5246	Kirensk	Russia	KCK
5247	Ust-Kut	Russia	UKX
5254	Taganrog	Russia	TGK
6404	Bodaibo	Russia	ODO

Top 15 Indian airports with least incoming flights

India_in_deg = {}
for x in all_in_deg.keys():
    if x in Ind_Iata:
        India_in_deg[x] = all_in_deg[x]
top = sorted(India_in_deg.items(), key=operator.itemgetter(1))
indian_in_deg_df = pd.DataFrame(top, columns = ["IATA", "In Degree"])
indian_in_deg_df.head(15)

	IATA	In Degree
0	BHJ	0.000292
1	BHU	0.000292
2	DIU	0.000292
3	RAJ	0.000292
4	GOP	0.000292
5	PBD	0.000292
6	RJA	0.000292
7	HJR	0.000292
8	AGX	0.000292
9	GWL	0.000292
10	JGA	0.000292
11	SHL	0.000292
12	DHM	0.000292
13	KNU	0.000292
14	KUU	0.000292

Top 5 airports with the most outgoing flights

top5=sorted(out_deg.items(), key=operator.itemgetter(1),reverse=True)[:5]
print("Out-Degree Values:\n",top5)
l=[]
for i,j in top5:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

Out-Degree Values:
 [('FRA', 0.07127945123769759), ('CDG', 0.07068297047420222), ('AMS', 0.06919176856546377), ('IST', 0.06770056665672533), ('ATL', 0.06471816283924844)]

	City	Country	IATA
336	Frankfurt	Germany	FRA
574	Amsterdam	Netherlands	AMS
1346	Paris	France	CDG
3482	Atlanta	United States	ATL
7629	Istanbul	Turkey	IST

Top 15 Indian airports with most outgoing flights

India_out_deg = {}
for x in all_out_deg.keys():
    if x in Ind_Iata:
        India_out_deg[x] = all_out_deg[x]
top = sorted(India_out_deg.items(), key=operator.itemgetter(1),reverse=True)
indian_out_deg_df = pd.DataFrame(top, columns = ["IATA", "Out Degree"])
indian_out_deg_df.head(15)

	IATA	Out Degree
0	DEL	0.028329
1	BOM	0.024241
2	BLR	0.011682
3	MAA	0.011682
4	CCU	0.010806
5	HYD	0.009346
6	COK	0.006425
7	AMD	0.005257
8	CCJ	0.005257
9	TRV	0.004965
10	GOI	0.003505
11	PNQ	0.003505
12	GAU	0.003505
13	JAI	0.002629
14	LKO	0.002629

Top 5 airports with the least outgoing flights

bot5=sorted(out_deg.items(), key=operator.itemgetter(1))[:5]
print("Out-Degree Values:\n",bot5)
l=[]
for i,j in bot5:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

Out-Degree Values:
 [('TGK', 0.00029824038174768865), ('KCK', 0.00029824038174768865), ('ODO', 0.00029824038174768865), ('UKX', 0.00029824038174768865), ('AYP', 0.00029824038174768865)]

	City	Country	IATA
2648	Ayacucho	Peru	AYP
5246	Kirensk	Russia	KCK
5247	Ust-Kut	Russia	UKX
5254	Taganrog	Russia	TGK
6404	Bodaibo	Russia	ODO

Top 15 Indian airports with least outgoing flights

India_out_deg = {}
for x in all_out_deg.keys():
    if x in Ind_Iata:
        India_out_deg[x] = all_out_deg[x]
top = sorted(India_out_deg.items(), key=operator.itemgetter(1))
indian_out_deg_df = pd.DataFrame(top, columns = ["IATA", "Out Degree"])
indian_out_deg_df.head(15)

	IATA	Out Degree
0	BHJ	0.000292
1	BHU	0.000292
2	DIU	0.000292
3	RAJ	0.000292
4	GOP	0.000292
5	PBD	0.000292
6	RJA	0.000292
7	HJR	0.000292
8	AGX	0.000292
9	GWL	0.000292
10	JGA	0.000292
11	SHL	0.000292
12	DHM	0.000292
13	KNU	0.000292
14	KUU	0.000292

Closeness Centrality

closeness = nx.closeness_centrality(G_sc, wf_improved=True)

Airports that allows us to reach all other airports with the lowest average number of airports in between

close=sorted(closeness.items(), key=operator.itemgetter(1),reverse=True)[:5]
l=[]
for i,j in close:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

	City	Country	IATA
336	Frankfurt	Germany	FRA
502	London	United Kingdom	LHR
574	Amsterdam	Netherlands	AMS
1346	Paris	France	CDG
2100	Dubai	United Arab Emirates	DXB

Top 15 Indian airports with highest closeness centrality

India_close = {}
for x in closeness.keys():
    if x in Ind_Iata:
        India_close[x] = closeness[x]
top = sorted(India_close.items(), key=operator.itemgetter(1),reverse=True)
indian_close_df = pd.DataFrame(top, columns = ["IATA", "Closeness"])
indian_close_df.head(15)

	IATA	Closeness
0	DEL	0.348508
1	BOM	0.332903
2	BLR	0.315814
3	MAA	0.312401
4	HYD	0.305457
5	CCU	0.298363
6	PNQ	0.294407
7	COK	0.293762
8	AMD	0.293274
9	TRV	0.292966
10	GOI	0.291743
11	CCJ	0.288133
12	LKO	0.285168
13	IXE	0.283552
14	ATQ	0.277245

Airports that allows us to reach all other airports with the highest average number of airports in between

close=sorted(closeness.items(), key=operator.itemgetter(1))[:18]
l=[]
for i,j in close:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

	City	Country	IATA
9	Thule	Greenland	THU
1009	Isiro	Congo (Kinshasa)	IRP
4228	Peawanuck	Canada	YPO
4241	Salluit	Canada	YZG
5409	Perryville	United States	KPV

Top 15 Indian airports with lowest closeness centrality

India_close = {}
for x in closeness.keys():
    if x in Ind_Iata:
        India_close[x] = closeness[x]
top = sorted(India_close.items(), key=operator.itemgetter(1))
indian_close_df = pd.DataFrame(top, columns = ["IATA", "Closeness"])
indian_close_df.head(15)

	IATA	Closeness
0	PBD	0.199893
1	HJR	0.199905
2	AGX	0.227076
3	SHL	0.229815
4	DMU	0.229831
5	IXA	0.229846
6	AJL	0.229846
7	IXS	0.229846
8	IMF	0.229878
9	RJA	0.234001
10	TIR	0.234001
11	IXZ	0.239517
12	IXG	0.240031
13	MYQ	0.240031
14	VGA	0.240117

Betweenness Centrality

betweeness = nx.betweenness_centrality(G_sc, normalized=True)

Airports that most often act as a bridge between other pairs of airports

betw=sorted(betweeness.items(), key=operator.itemgetter(1),reverse=True)[:5]
l=[]
for i,j in betw:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

	City	Country	IATA
336	Frankfurt	Germany	FRA
1346	Paris	France	CDG
2100	Dubai	United Arab Emirates	DXB
3285	Los Angeles	United States	LAX
3574	Anchorage	United States	ANC

Top 15 Indian Airports that most often act as a bridge between other pairs of airports

ind_betweeness = {}
for x in betweeness.keys():
    if x in Ind_Iata:
        ind_betweeness[x] = betweeness[x]
top = sorted(ind_betweeness.items(), key=operator.itemgetter(1),reverse=True)
indian_betw_df = pd.DataFrame(top, columns = ["IATA", "Betweenness"])
indian_betw_df.head(15)

	IATA	Betweenness
0	DEL	0.015452
1	BOM	0.010391
2	CCU	0.004037
3	BLR	0.002234
4	HYD	0.001721
5	MAA	0.001704
6	COK	0.000616
7	TRV	0.000592
8	GAU	0.000409
9	VNS	0.000304
10	DIU	0.000298
11	AGR	0.000298
12	PBD	0.000298
13	CCJ	0.000282
14	LKO	0.000038

Articulation Point & Bridges

• A node is considered an articulation point if the removal of that node (along with all its incident edges) increases the number of disconnected components of a network
• A bridge in a graph is an edge whose removal causes the number of connected components of the graph to increase.

arti=list(nx.articulation_points(G_sc.to_undirected()))
print("No of Articulation Points in the graph: ",len(arti))

No of Articulation Points in the graph:  331

Articulation Points in Indian Air Traffic Network

for x in arti:
    if x in Ind_Iata:
        print(x)

MAA
COK
HYD
BOM
BLR
CCU
DEL

nx.has_bridges(G.to_undirected())
#we can infer that the graph has bridges

True

No of airports pairs that act as bridges

l = list(nx.bridges(G_sc.to_undirected()))
final_l = []
for x in l:
    for y in x:
        if y in Ind_Iata:
            final_l.append(x)
for x in set(final_l):
    print(x)

('BOM', 'RAJ')
('COK', 'AGX')
('BOM', 'BHJ')
('BLR', 'MYQ')
('BOM', 'GWL')
('BLR', 'IXG')
('BOM', 'JGA')
('BOM', 'BHU')
('HYD', 'RJA')
('HYD', 'TIR')
('DEL', 'KNU')
('MAA', 'TCR')
('DEL', 'GOP')
('DEL', 'KUU')
('CCU', 'SHL')
('DEL', 'DHM')
('DEL', 'LUH')

l = list(nx.bridges(G_sc.to_undirected()))
print("No of Bridges in the graph: ",len(l))

No of Bridges in the graph:  764

PageRank

Top 5 aiports with highest pagerank

pr = nxo.pagerank(G_sc, alpha=0.85)
#print("Pagerank values:\n",pr)
pager=sorted(pr.items(), key=operator.itemgetter(1),reverse=True)[:5]
l=[]
for i,j in pager:
    l.append(i)
airport_df.loc[airport_df['IATA'].isin(l)]

	City	Country	IATA
3470	Dallas-Fort Worth	United States	DFW
3482	Atlanta	United States	ATL
3551	Denver	United States	DEN
3630	Chicago	United States	ORD
7629	Istanbul	Turkey	IST

Top 15 Indian Aiports with highest pagerank

ind_pgr = {}
for x in pr.keys():
    if x in Ind_Iata:
        ind_pgr[x] = pr[x]
top = sorted(ind_pgr.items(), key=operator.itemgetter(1),reverse=True)
indian_pgr_df = pd.DataFrame(top, columns = ["IATA", "Page Rank"])
indian_pgr_df.head(15)

	IATA	Page Rank
0	DEL	0.002358
1	BOM	0.002124
2	CCU	0.001041
3	BLR	0.000952
4	MAA	0.000847
5	HYD	0.000765
6	COK	0.000492
7	GAU	0.000406
8	AMD	0.000387
9	TRV	0.000386
10	CCJ	0.000356
11	PNQ	0.000301
12	GOI	0.000271
13	LKO	0.000250
14	VTZ	0.000232

HITS

hits = nxo.hits(G_sc)
#hits

Good Hubs & Authorities

• Good Hubs are those nodes that are good at pointing at good authorities
• Good Authorities are those nodes that have higher authority scores and higher no of incoming nodes

# hits_df = pd.DataFrame(columns=["IATA","Hub", "Authority"])
# l = len(hits[0])
# i = 0
# for x in Ind_Iata:
#     if hits[0].get(x):
#         hits_df.loc[i] = [x, hits[0][x], hits[1][x]]
#         i+=1
# print(hits_df)

for x in Ind_Iata:
    if hits[1].get(x):
        print(x," : ",hits[0][x], ", ", hits[1][x])

AMD  :  0.00041462102803375327 ,  0.00041550457987932416
IXU  :  6.247356125536419e-05 ,  6.250839461143293e-05
BOM  :  0.001890043776088612 ,  0.0018771079713944796
BHJ  :  2.713736921784039e-05 ,  2.7207616139343914e-05
IXG  :  1.3362506926185294e-05 ,  1.3109270493790411e-05
BDQ  :  6.247356125536419e-05 ,  6.250839461143293e-05
BHO  :  6.247356125536419e-05 ,  6.36969053664013e-05
BHU  :  2.713736921784039e-05 ,  2.7207616139343914e-05
GOI  :  0.00032430342034123807 ,  0.0003232198223312108
IDR  :  9.152597618347638e-05 ,  8.965009197877407e-05
JLR  :  6.247356125536419e-05 ,  6.250839461143293e-05
JGA  :  2.713736921784039e-05 ,  2.7207616139343914e-05
HJR  :  1.1334955847956406e-06 ,  3.907300053579009e-07
NAG  :  0.00010250290657184083 ,  9.998670963101149e-05
PNQ  :  0.00013875196602425823 ,  0.00025430522265859563
PBD  :  2.713736921784039e-05 ,  1.062073992323441e-126
RAJ  :  2.713736921784039e-05 ,  2.7207616139343914e-05
RPR  :  8.256281416670241e-05 ,  8.109302258213618e-05
STV  :  6.247356125536419e-05 ,  6.250839461143293e-05
UDR  :  6.247356125536419e-05 ,  6.250839461143293e-05
IXA  :  8.409033717413222e-06 ,  8.406423840358991e-06
AJL  :  8.409033717413222e-06 ,  8.406423840358991e-06
IXB  :  8.826770064423606e-05 ,  8.820651004185008e-05
BBI  :  9.400278614579659e-05 ,  9.34367181567115e-05
CCU  :  0.00044601861653560687 ,  0.0004444718815910611
IMF  :  8.648647053778212e-06 ,  8.645074511314434e-06
IXS  :  8.409033717413222e-06 ,  8.406423840358991e-06
DIB  :  8.378617302497139e-06 ,  4.368429420001272e-05
PAT  :  4.569716043552527e-05 ,  7.32330367190786e-05
IXR  :  6.995802657551156e-05 ,  4.237914050732273e-05
VTZ  :  0.00014084634357974446 ,  0.00014187138025884466
AGR  :  2.7143018005540127e-05 ,  2.8342308210044794e-05
IXD  :  6.247356125536419e-05 ,  6.250839461143293e-05
ATQ  :  0.00015039652217336527 ,  0.00015113990888699535
VNS  :  7.882416728502808e-05 ,  7.840456385733869e-05
KUU  :  3.533619203752379e-05 ,  3.530077847208902e-05
IXC  :  6.344281156422344e-05 ,  6.347343969741238e-05
KNU  :  3.533619203752379e-05 ,  3.530077847208902e-05
DED  :  3.8658750209739533e-05 ,  3.8597817780083014e-05
DEL  :  0.0024522551443017333 ,  0.002444225422881305
GWL  :  2.713736921784039e-05 ,  2.7207616139343914e-05
JDH  :  6.247356125536419e-05 ,  6.250839461143293e-05
JAI  :  0.0001757917212526902 ,  0.0001756517489665523
IXJ  :  6.398042204124636e-05 ,  6.400938603588401e-05
LUH  :  3.533619203752379e-05 ,  3.530077847208902e-05
IXL  :  3.7230826697594686e-05 ,  3.718683649192804e-05
LKO  :  0.00022903692082696227 ,  0.0002298233251876132
SXR  :  6.703922346787582e-05 ,  6.704362041992628e-05
AGX  :  7.124532459547811e-06 ,  7.099779847154677e-06
BLR  :  0.0009106676225844371 ,  0.0009242925527381857
VGA  :  2.2758050164709746e-05 ,  2.2480267070306176e-05
CJB  :  0.0001524885093139294 ,  0.00015310981105062075
COK  :  0.0004932036178030952 ,  0.000492808148237303
CCJ  :  0.0003867304924932324 ,  0.0003681632735463718
IXM  :  4.017919728006637e-05 ,  4.0153515755753365e-05
IXE  :  0.00024022201238421375 ,  0.00018559351414130715
MAA  :  0.000874621537185458 ,  0.0008613526754240658
IXZ  :  5.4214511067364636e-05 ,  2.0364111439760188e-05
RJA  :  9.395543238524452e-06 ,  9.370996576515765e-06
TIR  :  9.395543238524452e-06 ,  9.370996576515765e-06
TRZ  :  9.263634603315054e-05 ,  9.26477122726283e-05
TRV  :  0.00041411456158988905 ,  0.0004151621275389458
DIU  :  1.535441461310059e-128 ,  2.7207616139343914e-05
HBX  :  4.049987614402569e-05 ,  4.031688663313433e-05
SHL  :  6.425734556294226e-06 ,  6.4205408696469186e-06
GAU  :  0.00012930558346772498 ,  0.00012854090822385044
DMU  :  7.057278846123135e-06 ,  6.541152998440369e-06
GOP  :  3.533619203752379e-05 ,  3.530077847208902e-05
DHM  :  3.533619203752379e-05 ,  3.530077847208902e-05
MYQ  :  1.3362506926185294e-05 ,  1.3109270493790411e-05
HYD  :  0.0006509792575876167 ,  0.0006498953147242205

Analysis & Results

df = pd.DataFrame()
df['IATA'] = G_sc.nodes()
df['clustering'] = pd.Series(nx.clustering(G_sc))
df['in_degree'] = pd.Series(dict(in_deg))
df['out_degree'] = pd.Series(dict(out_deg))
df['degree_centrality'] = pd.Series(nx.degree_centrality(G))
df['closeness'] = pd.Series(closeness)
df['betweeness'] = pd.Series(betweeness)
df['pr'] = pd.Series(pr)
df['hits_hubs'] = pd.Series(hits[0])
df['hits_auth'] = pd.Series(hits[1])

df.head(10)

	IATA	clustering	in_degree	out_degree	degree_centrality	closeness	betweeness	pr	hits_hubs	hits_auth
AER	AER	0.525952	0.005368	0.005070	0.010222	0.281836	2.529012e-05	0.000347	0.000435	0.000437
KZN	KZN	0.423810	0.006263	0.006263	0.012266	0.283672	5.094607e-05	0.000409	0.000428	0.000429
ASF	ASF	0.571429	0.002088	0.002088	0.004089	0.271740	8.119928e-07	0.000163	0.000229	0.000228
MRV	MRV	0.495833	0.004772	0.004772	0.009346	0.292022	3.069963e-05	0.000315	0.000475	0.000478
CEK	CEK	0.538462	0.004175	0.004175	0.008178	0.273558	4.533672e-06	0.000277	0.000325	0.000327
OVB	OVB	0.259246	0.015807	0.015508	0.030666	0.316769	1.832225e-03	0.001051	0.001094	0.001106
DME	DME	0.074136	0.056367	0.056367	0.110397	0.341272	3.050986e-02	0.004160	0.003685	0.003677
NBC	NBC	0.809524	0.002088	0.002088	0.004089	0.280609	6.785994e-07	0.000162	0.000307	0.000305
TGK	TGK	0.000000	0.000298	0.000298	0.000584	0.254459	0.000000e+00	0.000063	0.000053	0.000053
UUA	UUA	0.666667	0.000895	0.000895	0.001752	0.259279	7.711068e-08	0.000101	0.000101	0.000101

Ind = airport_df[airport_df['Country'] == 'India']
# Ind_cities = Ind['City'].unique()
# Ind_Iata = Ind['IATA'].unique()
final_df = df[df['IATA'].isin(Ind_Iata)]
final_df

	IATA	clustering	in_degree	out_degree	degree_centrality	closeness	betweeness	pr	hits_hubs	hits_auth
CCU	CCU	0.239489	0.011035	0.011035	0.021612	0.298363	0.004037	0.001041	0.000446	0.000444
DEL	DEL	0.187544	0.028929	0.028929	0.056659	0.348508	0.015452	0.002358	0.002452	0.002444
AMD	AMD	0.702614	0.005368	0.005368	0.010514	0.293274	0.000022	0.000387	0.000415	0.000416
BLR	BLR	0.374063	0.012228	0.011930	0.023657	0.315814	0.002234	0.000952	0.000911	0.000924
BOM	BOM	0.202045	0.024456	0.024754	0.048189	0.332903	0.010391	0.002124	0.001890	0.001877
...	...	...	...	...	...	...	...	...	...	...
TIR	TIR	0.000000	0.000298	0.000298	0.000584	0.234001	0.000000	0.000065	0.000009	0.000009
VGA	VGA	1.000000	0.000596	0.000596	0.001168	0.240117	0.000000	0.000085	0.000023	0.000022
HBX	HBX	1.000000	0.000596	0.000596	0.001168	0.249851	0.000000	0.000087	0.000040	0.000040
IXG	IXG	0.000000	0.000298	0.000298	0.000584	0.240031	0.000000	0.000065	0.000013	0.000013
MYQ	MYQ	0.000000	0.000298	0.000298	0.000584	0.240031	0.000000	0.000065	0.000013	0.000013

71 rows × 10 columns

final_df.to_csv('final_result.csv')

Visualising the Air Route & Aiport Network of India

Top 20 airlines that fly in and out of India

1. JET Airways and AIR INDIA are the undisputed leaders in international flights
2. Emirates comes a close third. Top 4 operators make up 50 % of the total share.
3. The top 10 operators is dominated by middle east flight operators.

df_traffic_monthly = pd.read_csv("Airlinewise Monthly International Air Traffic To And From The Indian Territory.csv")
df_traffic_passengers = df_traffic_monthly[(df_traffic_monthly['PASSENGERS FROM INDIA'] > 0) | (df_traffic_monthly['PASSENGERS TO INDIA'] > 0)]
df_traffic_passengers_yearly = df_traffic_passengers.groupby(['AIRLINE NAME', 'YEAR'])['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'].sum().reset_index()
df_traffic_passengers_yearly['TOTAL TRAFFIC'] = df_traffic_passengers_yearly['PASSENGERS TO INDIA'] + df_traffic_passengers_yearly['PASSENGERS FROM INDIA']
#df_traffic_passengers_from = df_traffic_passengers.groupby(['AIRLINE NAME', 'YEAR'])[].sum().nlargest(20).reset_index(name='FROM COUNT')
#df_traffic_passengers_combined = pd.merge(df_traffic_passengers_to, df_traffic_passengers_from, on='AIRLINE NAME')
#df_traffic_passengers_combined['TOTAL TRAFFIC'] = df_traffic_passengers_combined['FROM COUNT'] + df_traffic_passengers_combined['TO COUNT']
#df_traffic_passengers_combined['OUTBOUND TO INBOUND RATIO'] = df_traffic_passengers_combined['FROM COUNT'] / df_traffic_passengers_combined['TO COUNT']
#df_traffic_passengers_combined.plot(x='AIRLINE NAME', y='TOTAL TRAFFIC', kind='pie', legend=False, autopct='%1.1f%%', labels=df_traffic_passengers_to['AIRLINE NAME'], figsize=(10, 10), title='Top 10 Carrying passengers to India')
df_traffic_2015 = df_traffic_passengers_yearly[df_traffic_passengers_yearly['YEAR'] == 2015]
df_traffic_2015_top10 = df_traffic_2015[df_traffic_2015['TOTAL TRAFFIC'].isin(df_traffic_2015['TOTAL TRAFFIC'].nlargest(10))]
df_traffic_2016 = df_traffic_passengers_yearly[df_traffic_passengers_yearly['YEAR'] == 2016]
df_traffic_2016_top10 = df_traffic_2016[df_traffic_2016['TOTAL TRAFFIC'].isin(df_traffic_2016['TOTAL TRAFFIC'].nlargest(10))]
df_traffic_2017 = df_traffic_passengers_yearly[df_traffic_passengers_yearly['YEAR'] == 2017]
df_traffic_2017_top10 = df_traffic_2017[df_traffic_2017['TOTAL TRAFFIC'].isin(df_traffic_2017['TOTAL TRAFFIC'].nlargest(10))]
fig = plt.figure()
fig.subplots_adjust(hspace=1.2, wspace=.5)
ax = plt.subplot(221)
df_traffic_2015_top10.plot(ax=ax, x='AIRLINE NAME', y='TOTAL TRAFFIC', kind='bar', figsize=(12, 8), title='2015 Traffic')
ax = plt.subplot(222)
df_traffic_2016_top10.plot(ax=ax, x='AIRLINE NAME', y='TOTAL TRAFFIC', kind='bar', title='2016 Traffic')
ax = plt.subplot(223)
df_traffic_2017_top10.plot(ax=ax, x='AIRLINE NAME', y='TOTAL TRAFFIC', kind='bar', title='2017 Q1 Traffic')

<AxesSubplot:title={'center':'2017 Q1 Traffic'}, xlabel='AIRLINE NAME'>

Visualising Month-wise Passenger Traffic & Quarter-wise Passenger Traffic

1. Q1 is undoubtedly the best quarter for air traffic in India
2. There is not much difference when we look at other quarter numbers.

fig, axes = plt.subplots(nrows=1, ncols=2)
df_qtrly = df_traffic_passengers.groupby(['QUARTER'])['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'].sum().reset_index()
df_qtrly.plot(x='QUARTER', y=['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'], kind='bar', ax=axes[0], figsize=(12, 7))
df_monthly = df_traffic_passengers.groupby(['MONTH'])['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'].sum().reset_index()
df_monthly.plot(x='MONTH', y=['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'], kind='bar', ax=axes[1], figsize=(12, 7))

<AxesSubplot:xlabel='MONTH'>

Visualising the Rate of change of Passenger Traffic

1. We already saw that Q1 has a lion's share of passenger traffic to and from India
2. Looking at Q1 numbers over 3 years, we can see that passenger traffic has been increasing both ways.
3. Passengers flying in to India has increased by 18.5% over 3 years whereas Passngers flying out from India has increased by 16.3%.

df_yearly = df_traffic_passengers.groupby(['YEAR', 'QUARTER'])['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'].sum().reset_index()
df_yearly = df_yearly[df_yearly['QUARTER'] == 'Q1']
df_yearly.plot(x='YEAR', y=['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'], kind='line', figsize=(10, 10))
df_yearly

	YEAR	QUARTER	PASSENGERS TO INDIA	PASSENGERS FROM INDIA
0	2015	Q1	5712230.0	6376365
4	2016	Q1	6343530.0	6871288
8	2017	Q1	6778304.0	7424671

Visualising the Quarterly traffic of top 10 airlines

1. The top 4 remain the same during all 4 quarters.
2. Infact the ratio of traffic between the top 10 airlines is almost the same
3. Qatar Airways earns a spot in the top 10 in the last quarter.

df_qtrly_airlines = df_traffic_passengers.groupby(['QUARTER', 'AIRLINE NAME'])['PASSENGERS TO INDIA', 'PASSENGERS FROM INDIA'].sum().reset_index()
df_qtrly_airlines['TOTAL'] = df_qtrly_airlines['PASSENGERS TO INDIA'] + df_qtrly_airlines['PASSENGERS FROM INDIA'] 
df_airlines_q1 = df_qtrly_airlines[df_qtrly_airlines['QUARTER'] == 'Q1']
df_q1_airlines_top10 = df_airlines_q1[df_airlines_q1['TOTAL'].isin(df_airlines_q1['TOTAL'].nlargest(10))]
df_airlines_q2 = df_qtrly_airlines[df_qtrly_airlines['QUARTER'] == 'Q2']
df_q2_airlines_top10 = df_airlines_q2[df_airlines_q2['TOTAL'].isin(df_airlines_q2['TOTAL'].nlargest(10))]
df_airlines_q3 = df_qtrly_airlines[df_qtrly_airlines['QUARTER'] == 'Q3']
df_q3_airlines_top10 = df_airlines_q3[df_airlines_q3['TOTAL'].isin(df_airlines_q3['TOTAL'].nlargest(10))]
df_airlines_q4 = df_qtrly_airlines[df_qtrly_airlines['QUARTER'] == 'Q4']
df_q4_airlines_top10 = df_airlines_q4[df_airlines_q4['TOTAL'].isin(df_airlines_q4['TOTAL'].nlargest(10))]
fig, axes = plt.subplots(nrows=2, ncols=2)
df_q1_airlines_top10.plot(x='AIRLINE NAME', y=['TOTAL'], kind='bar', ax=axes[0,0], figsize=(15, 20), title = 'Q1 Traffic')
df_q2_airlines_top10.plot(x='AIRLINE NAME', y=['TOTAL'], kind='bar', ax=axes[0,1], title = 'Q2 Traffic')
df_q3_airlines_top10.plot(x='AIRLINE NAME', y=['TOTAL'], kind='bar', ax=axes[1,0], title = 'Q3 Traffic')
df_q4_airlines_top10.plot(x='AIRLINE NAME', y=['TOTAL'], kind='bar', ax=axes[1,1], title = 'Q4 Traffic')

<AxesSubplot:title={'center':'Q4 Traffic'}, xlabel='AIRLINE NAME'>

Visualising City-wise traffic based on departure and arrivals

df_citywise = pd.read_csv("Citypairwise Quarterly International  Air Traffic To And From The Indian Territory.csv", error_bad_lines=False, warn_bad_lines=False)
df_citywise = df_citywise.iloc[:, :-3]
df_citywise['TOTAL TRAFFIC'] = df_citywise['PASSENGERS FROM CITY1 TO CITY2'] + df_citywise['PASSENGERS FROM CITY2 TO CITY1']
df_citywise.head(10)

	YEAR	QUARTER	CITY1	CITY2	PASSENGERS FROM CITY1 TO CITY2	PASSENGERS FROM CITY2 TO CITY1	TOTAL TRAFFIC
0	2015	Q1	ABUDHABI	AHMEDABAD	12764	15358	28122
1	2015	Q1	ABUDHABI	BENGALURU	27341	35988	63329
2	2015	Q1	ABUDHABI	CHENNAI	31756	32593	64349
3	2015	Q1	ABUDHABI	DELHI	66416	65956	132372
4	2015	Q1	ABUDHABI	GOA	8408	12593	21001
5	2015	Q1	ABUDHABI	HYDERABAD	37265	44645	81910
6	2015	Q1	ABUDHABI	JAIPUR	9437	10859	20296
7	2015	Q1	ABUDHABI	KOCHI	34055	41972	76027
8	2015	Q1	ABUDHABI	KOLKATA	1580	1799	3379
9	2015	Q1	ABUDHABI	KOZHIKODE	31618	41032	72650

Top 100 airport pairs with highest traffic inflow and outflow combined

Below is a snapshot of 20 of them

df_citywise_top100 = df_citywise[df_citywise['TOTAL TRAFFIC'].isin(df_citywise['TOTAL TRAFFIC'].nlargest(100))]
df_citywise_top100.head(20)

	YEAR	QUARTER	CITY1	CITY2	PASSENGERS FROM CITY1 TO CITY2	PASSENGERS FROM CITY2 TO CITY1	TOTAL TRAFFIC
42	2015	Q1	BANGKOK	MUMBAI	90096	94099	184195
93	2015	Q1	DUBAI	CHENNAI	102190	118103	220293
94	2015	Q1	DUBAI	DELHI	218394	218530	436924
96	2015	Q1	DUBAI	HYDERABAD	99989	112952	212941
98	2015	Q1	DUBAI	KOCHI	99921	114833	214754
104	2015	Q1	DUBAI	MUMBAI	260038	300106	560144
141	2015	Q1	KATHMANDU	DELHI	91764	86994	178758
172	2015	Q1	LONDON	DELHI	122349	122506	244855
174	2015	Q1	LONDON	MUMBAI	83375	94554	177929
266	2015	Q1	SINGAPORE	CHENNAI	88291	100794	189085
268	2015	Q1	SINGAPORE	DELHI	86479	97730	184209
272	2015	Q1	SINGAPORE	MUMBAI	79324	94249	173573
324	2015	Q2	BANGKOK	DELHI	85201	89927	175128
328	2015	Q2	BANGKOK	MUMBAI	92270	90960	183230
341	2015	Q2	COLOMBO	CHENNAI	88976	94312	183288
378	2015	Q2	DUBAI	BENGALURU	91594	88399	179993
379	2015	Q2	DUBAI	CHENNAI	117706	119990	237696
380	2015	Q2	DUBAI	DELHI	203463	219902	423365
382	2015	Q2	DUBAI	HYDERABAD	110757	117835	228592
384	2015	Q2	DUBAI	KOCHI	129829	121029	250858

Graphical depiction of busiest airport pairs

1. Dubai - Mumbai is arguably the busiest route with a whopping 4.5 million passengers flying both ways in 3 years
2. Take a look a the table below the graph. Flights fly in and out of Dubai to 5 Indian cities of the top 10 airport pairs.
3. A considerable number of flights fly in an out of London and (Delhi, Mumbai)

df_citywise_cumulative_traffic = df_citywise.groupby(['CITY1', 'CITY2'])['TOTAL TRAFFIC'].sum().nlargest(15).reset_index()
highest_traffic = np.max(df_citywise['TOTAL TRAFFIC'])
uniqueCity1, city1Ints = np.unique(df_citywise_cumulative_traffic['CITY1'], return_inverse=True)
uniqueCity2, city2Ints = np.unique(df_citywise_cumulative_traffic['CITY2'], return_inverse=True)
df_citywise_cumulative_traffic['TOTAL TRAFFIC SCALED'] = (df_citywise_cumulative_traffic['TOTAL TRAFFIC'] / highest_traffic) * 100
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.scatter(city1Ints, city2Ints, df_citywise_cumulative_traffic['TOTAL TRAFFIC'], s=df_citywise_cumulative_traffic['TOTAL TRAFFIC SCALED'], c='b')
ax.set(xticks=range(len(uniqueCity1)), xticklabels=uniqueCity1,
       yticks=range(len(uniqueCity2)), yticklabels=uniqueCity2) 
plt.show()
df_citywise_cumulative_traffic.head(10)

	CITY1	CITY2	TOTAL TRAFFIC	TOTAL TRAFFIC SCALED
0	DUBAI	MUMBAI	4480329	739.172089
1	DUBAI	DELHI	3659375	603.729740
2	DUBAI	KOCHI	2085457	344.062145
3	LONDON	DELHI	1953307	322.259820
4	DUBAI	HYDERABAD	1886814	311.289695
5	DUBAI	CHENNAI	1846427	304.626581
6	LONDON	MUMBAI	1686086	278.173257
7	ABUDHABI	MUMBAI	1524384	251.495394
8	BANGKOK	DELHI	1511137	249.309882
9	DUBAI	BENGALURU	1455623	240.151090

Visualising the Flight map of all Air Traffic flowing in and out of India

os.environ["PROJ_LIB"] = "C:\\Users\\Rahul\\anaconda3\\Library\\share"; #fixr
def plot_map(in_filename, color_mode='screen',
             out_filename='flights_map_mpl.png', absolute=False):
    if color_mode == 'screen':
        bg_color = (1.0, 1.0, 1, 1.0)
        coast_color = (104/255.0, 0, 153/255.0, 0.7)
        color_list = [(0.0, 0.0, 0.0, 0.0),
                      (204/255.0, 0, 153/255.0, 0.6),
                      (255/255.0, 204/255.0, 230/255.0, 1.0)]
    else:
        bg_color = (1.0, 1.0, 1.0, 0.5)
        coast_color = (10.0/255.0, 10.0/255.0, 10/255.0, 0.8)
        color_list = [(1.0, 1.0, 1.0, 0.0),
                      (255/255.0, 204/255.0, 230/255.0, 1.0),
                      (204/255.0, 0, 153/255.0, 0.6)
                      ]

    # define the expected CSV columns
    CSV_COLS = ('dep_lat', 'dep_lon', 'arr_lat', 'arr_lon',
                'nb_flights', 'CO2')
    routes = pd.read_csv(in_filename, names=CSV_COLS, na_values=['\\N'],
                         sep=',', skiprows=1)
    num_routes = len(routes.index)

    # normalize the dataset for color scale
    norm = PowerNorm(0.3, routes['nb_flights'].min(),
                     routes['nb_flights'].max())
    # norm = Normalize(routes['nb_flights'].min(), routes['nb_flights'].max())
    # create a linear color scale with enough colors
    if absolute:
        n = routes['nb_flights'].max()
    else:
        n = num_routes
    cmap = LinearSegmentedColormap.from_list('cmap_flights', color_list,
                                             N=n)
    # create the map and draw country boundaries
    plt.figure(figsize=(27, 20))
    m = Basemap(projection='mill', lon_0=0)
    
#     m.drawcountries(linewidth = 1.25, color = (123, 123, 123, 123))
#     m.drawcoastlines(color=coast_color, linewidth=1.25)
    m.fillcontinents(color=bg_color, lake_color=bg_color)
    m.drawcountries(linewidth = 1.25, color = (0, 0, 0, 0.5))
#     m.drawstates(linewidth = 0.2)
    m.drawcoastlines(linewidth=2, color=coast_color)
    m.drawmapboundary(fill_color=bg_color)

    # plot each route with its color depending on the number of flights
    j = 1
    for i, route in enumerate(routes.sort_values(by='nb_flights',
                              ascending=True).iterrows()):
        j+=1
        if j ==85 or j==217:
            continue
        route = route[1]
        if absolute:
            color = cmap(norm(int(route['nb_flights'])))
        else:
            color = cmap(i * 1.0 / num_routes)

        line, = m.drawgreatcircle(route['dep_lon'], route['dep_lat'],
                                  route['arr_lon'], route['arr_lat'],
                                  linewidth=1, color=color)
        # if the path wraps the image, basemap plots a nasty line connecting
        # the points at the opposite border of the map.
        # we thus detect path that are bigger than 30km and split them
        # by adding a NaN
        path = line.get_path()
        cut_point, = np.where(np.abs(np.diff(path.vertices[:, 0])) > 30000e3)
        if len(cut_point) > 0:
            cut_point = cut_point[0]
            vertices = np.concatenate([path.vertices[:cut_point, :],
                                      [[np.nan, np.nan]],
                                      path.vertices[cut_point+1:, :]])
            path.codes = None  # treat vertices as a serie of line segments
            path.vertices = vertices

    # save the map
    plt.show()

plot_map('flight_data_new.csv', 'screen', absolute=True)

#defining CSV column names
CSV_COLS = ('dep_lat', 'dep_lon', 'arr_lat', 'arr_lon', 'nb_flights', 'CO2')
routes = pd.read_csv('flight_data.csv', names=CSV_COLS,na_values=['\\N'], sep=',', skiprows=1)

df = pd.read_csv("countrywise_quarterly_international_air_traffic_to_and_from_the_indian_territory.csv")
df

	year	quarter	country_name	passengers_to_india	passengers_from_india	freight_to_india	freight_from_india
0	2015	1	AFGHANISTAN	37194	32721	96.7	676.4
1	2015	1	AUSTRALIA	16676	19075	89.7	423.7
2	2015	1	AUSTRIA	13318	15529	563.8	707.0
3	2015	1	BAHRAIN	114087	133527	585.7	1909.5
4	2015	1	BANGLADESH	76357	75673	624.1	1052.9
...	...	...	...	...	...	...	...
494	2017	1	UNITED KINGDOM	311543	337464	13194.9	19492.1
495	2017	1	UNITED STATES	120375	142848	1666.3	2412.2
496	2017	1	UZBEKISTAN	12585	14158	949.6	1360.3
497	2017	1	VIETNAM	0	0	0.0	0.0
498	2017	1	YEMEN	1676	1543	0.0	14.4

499 rows × 7 columns

df["pti_scale"] = (df["passengers_to_india"] - np.min(df["passengers_to_india"])) / (np.max(df["passengers_to_india"]) - np.min(df["passengers_to_india"]))
df

	year	quarter	country_name	passengers_to_india	passengers_from_india	freight_to_india	freight_from_india	pti_scale
0	2015	1	AFGHANISTAN	37194	32721	96.7	676.4	0.015598
1	2015	1	AUSTRALIA	16676	19075	89.7	423.7	0.006993
2	2015	1	AUSTRIA	13318	15529	563.8	707.0	0.005585
3	2015	1	BAHRAIN	114087	133527	585.7	1909.5	0.047844
4	2015	1	BANGLADESH	76357	75673	624.1	1052.9	0.032022
...	...	...	...	...	...	...	...	...
494	2017	1	UNITED KINGDOM	311543	337464	13194.9	19492.1	0.130651
495	2017	1	UNITED STATES	120375	142848	1666.3	2412.2	0.050481
496	2017	1	UZBEKISTAN	12585	14158	949.6	1360.3	0.005278
497	2017	1	VIETNAM	0	0	0.0	0.0	0.000000
498	2017	1	YEMEN	1676	1543	0.0	14.4	0.000703

499 rows × 8 columns

plt.figure(figsize=(20, 20), dpi=80)
G = nx.Graph()
for index, row in df.iterrows():
    G.add_edge("India", index, weight=row['pti_scale'])
mn = df['pti_scale'].mean()
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > mn]
esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= mn]
pos = nx.spring_layout(G, seed=7)  # positions for all nodes - seed for reproducibility
# nodes
nx.draw_networkx_nodes(G, pos, node_size=70)
# edges
nx.draw_networkx_edges(G, pos, edgelist=elarge, width=5)
nx.draw_networkx_edges(
    G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color="b", style="dashed"
)
# labels
nx.draw_networkx_labels(G, pos, font_size=20, font_family="sans-serif")
ax = plt.gca()
ax.margins(0.08)
plt.axis("off")
plt.tight_layout()
plt.show()

Citywise Traffic Analysis

df1 = pd.read_csv("citypairwise_quarterly_international_air_traffic_to_and_from_the_indian_territory.csv")
df1["pfc1c2_scale"] = (df1["passengers_from_city1_to_city2"] - np.min(df1["passengers_from_city1_to_city2"])) / (np.max(df1["passengers_from_city1_to_city2"]) - np.min(df1["passengers_from_city1_to_city2"]))
df1["pfc2c1_scale"] = (df1["passengers_from_city2_to_city1"] - np.min(df1["passengers_from_city2_to_city1"])) / (np.max(df1["passengers_from_city2_to_city1"]) - np.min(df1["passengers_from_city2_to_city1"]))
pt_mean = df1["pfc1c2_scale"].mean()
city1_2 = df1["city2"].unique()
city2_1 = df1["city1"].unique()
print(city1_2)
print(city2_1)

['AHMEDABAD' 'BENGALURU' 'CHENNAI' 'DELHI' 'GOA' 'HYDERABAD' 'JAIPUR'
 'KOCHI' 'KOLKATA' 'KOZHIKODE' 'LUCKNOW' 'MANGALORE' 'MUMBAI' 'PUNE'
 'TRIVANDRUM' 'AMRITSAR' 'GAYA' 'VARANASI' 'MADURAI' 'TIRUCHIRAPALLY'
 'VISAKHAPATNAM' 'COIMBATORE' 'NAGPUR' 'CHANDIGARH']
['ABUDHABI' 'ADDIS ABABA' 'ADEN' 'AL AIN' 'ALMATY' 'AMSTERDAM' 'ASHGABAT'
 'BAHRAIN' 'BANGKOK' 'BIRMINGHAM' 'BISHKEK' 'BRUSELLS' 'CAIRO' 'CHICAGO'
 'CHITTAGONG' 'COLOGNE' 'COLOMBO' 'DAMMAM' 'DHAKA' 'DOHA' 'DUBAI'
 'DUSHANBE' 'FRANKFURT' 'GUANGZHOU' 'HAA DHAALU ATOLL' 'HELNSKI'
 'HO CHI MINH CITY' 'HONG KONG' 'ISTANBUL' 'JEDDAH' 'JOHANNESBERG' 'KABUL'
 'KARACHI' 'KATHMANDU' 'KUALALUMPUR' 'KUNMING' 'KUWAIT' 'LAHORE' 'LEIPZIG'
 'LONDON' 'LUXEMBOURG' 'MALE' 'MALPENSA' 'MANDALAY' 'MARUITIUS' 'MEDINA'
 'MELBOURNE' 'MOSCOW' 'MUENCHEN' 'MUSCAT' 'NAIROBI' 'NARITA' 'NAVOI'
 'NEW YORK' 'NEWARK' 'OSAKA' 'PARIS' 'PARO' 'PEKING' 'RANGOON' 'RIYADH'
 'ROME' 'SALALAH' 'SANAA' 'SEOUL' 'SEYCHELLES' 'SHANGHAI' 'SHARJAH'
 'SHENZHEN' 'SINGAPORE' 'SYDNEY' 'TAIPAE' 'TASHKENT' 'TEHRAN' 'TELAVIV'
 'TOKYO' 'TORONTO' 'VIENNA' 'ZURICH' 'BAGHDAD' 'REUNION' 'DON MUEANG'
 'KANDAHAR' 'MANCHESTER' 'SAN FRANCISCO' 'BASRA' 'RASALKHAMA' 'ANTALYA'
 'BAKU' 'MADRID' 'MASHHAD' 'TANGERANG' 'VANCOUVER']

for city in city1_2:
    plt.figure(figsize=(10, 10), dpi=80)
    G = nx.Graph()
    tot = 0
    for index, row in df1.iterrows():
        if row["city2"]==city:
            G.add_edge(row["city1"], row["city2"], weight=row['pfc1c2_scale'])
            tot+=row["passengers_from_city1_to_city2"]

    elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > pt_mean]
    esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= pt_mean]

    pos = nx.spring_layout(G, seed=7)  # positions for all nodes - seed for reproducibility

    # nodes
    nx.draw_networkx_nodes(G, pos, node_size=70)

    # edges
    nx.draw_networkx_edges(G, pos, edgelist=elarge, width=2, edge_color="r", style="dashed")
    nx.draw_networkx_edges(
        G, pos, edgelist=esmall, width=1, alpha=0.5, edge_color="b", style="dashed"
    )

    # labels
    nx.draw_networkx_labels(G, pos, font_size=12, font_family="sans-serif")

    ax = plt.gca()
    ax.margins(0.08)
    plt.axis("off")
    plt.tight_layout()
    plt.title(city + " - " + str(tot))
    plt.show()

G = nx.Graph()
for index, row in df.iterrows():
    G.add_edge("India", row["country_name"], weight=row['pti_scale'])
nx.degree_centrality(G)

{'India': 1.0,
 'AFGHANISTAN': 0.01694915254237288,
 'AUSTRALIA': 0.01694915254237288,
 'AUSTRIA': 0.01694915254237288,
 'BAHRAIN': 0.01694915254237288,
 'BANGLADESH': 0.01694915254237288,
 'BELGIUM': 0.01694915254237288,
 'BHUTAN': 0.01694915254237288,
 'BURMA': 0.01694915254237288,
 'CANADA': 0.01694915254237288,
 'CHINA': 0.01694915254237288,
 'EGYPT': 0.01694915254237288,
 'ETHIOPIA': 0.01694915254237288,
 'FINLAND': 0.01694915254237288,
 'FRANCE': 0.01694915254237288,
 'GEORGIA': 0.01694915254237288,
 'GERMANY': 0.01694915254237288,
 'HONG KONG': 0.01694915254237288,
 'IRAN': 0.01694915254237288,
 'IRAQ': 0.01694915254237288,
 'ISRAEL': 0.01694915254237288,
 'ITALY': 0.01694915254237288,
 'JAPAN': 0.01694915254237288,
 'JORDAN': 0.01694915254237288,
 'KAZAKHSTAN': 0.01694915254237288,
 'KENYA': 0.01694915254237288,
 'KOREA': 0.01694915254237288,
 'KUWAIT': 0.01694915254237288,
 'KYRGYZSTAN': 0.01694915254237288,
 'MALAYSIA': 0.01694915254237288,
 'MALDIVES': 0.01694915254237288,
 'MAURITIUS': 0.01694915254237288,
 'NEPAL': 0.01694915254237288,
 'NETHERLANDS': 0.01694915254237288,
 'OMAN': 0.01694915254237288,
 'PAKISTAN': 0.01694915254237288,
 'QATAR': 0.01694915254237288,
 'REUNION': 0.01694915254237288,
 'RUSSIA': 0.01694915254237288,
 'SAUDI ARABIA': 0.01694915254237288,
 'SEYCHELLES': 0.01694915254237288,
 'SINGAPORE': 0.01694915254237288,
 'SOUTH AFRICA': 0.01694915254237288,
 'SRI LANKA': 0.01694915254237288,
 'SWITZERLAND': 0.01694915254237288,
 'TAIWAN': 0.01694915254237288,
 'TAJIKISTAN': 0.01694915254237288,
 'THAILAND': 0.01694915254237288,
 'TURKEY': 0.01694915254237288,
 'TURKMENISTAN': 0.01694915254237288,
 'UNITED ARAB EMIRATES': 0.01694915254237288,
 'UNITED KINGDOM': 0.01694915254237288,
 'UNITED STATES': 0.01694915254237288,
 'UZBEKISTAN': 0.01694915254237288,
 'VIETNAM': 0.01694915254237288,
 'YEMEN': 0.01694915254237288,
 'UNITED ARAB EMIRATES*': 0.01694915254237288,
 'SPAIN': 0.01694915254237288,
 'AZERBAIJAN': 0.01694915254237288,
 'INDONESIA': 0.01694915254237288}

G1 = nx.Graph()
G2 = nx.Graph()
for index, row in df1.iterrows():
    G1.add_edge(row["city1"], row["city2"], weight=row['pfc1c2_scale'])
    G2.add_edge(row["city2"], row["city1"], weight=row['pfc2c1_scale'])
# print(nx.degree_centrality(G1))
# print(nx.degree_centrality(G2))
for city in city2_1:
    print(city, nx.degree_centrality(G2)[city])
print()
for city in city1_2:
    print(city, nx.degree_centrality(G2)[city])

ABUDHABI 0.12931034482758622
ADDIS ABABA 0.02586206896551724
ADEN 0.008620689655172414
AL AIN 0.017241379310344827
ALMATY 0.017241379310344827
AMSTERDAM 0.02586206896551724
ASHGABAT 0.017241379310344827
BAHRAIN 0.07758620689655173
BANGKOK 0.09482758620689655
BIRMINGHAM 0.017241379310344827
BISHKEK 0.008620689655172414
BRUSELLS 0.017241379310344827
CAIRO 0.008620689655172414
CHICAGO 0.017241379310344827
CHITTAGONG 0.008620689655172414
COLOGNE 0.017241379310344827
COLOMBO 0.09482758620689655
DAMMAM 0.07758620689655173
DHAKA 0.034482758620689655
DOHA 0.12068965517241378
DUBAI 0.1724137931034483
DUSHANBE 0.008620689655172414
FRANKFURT 0.06896551724137931
GUANGZHOU 0.017241379310344827
HAA DHAALU ATOLL 0.008620689655172414
HELNSKI 0.008620689655172414
HO CHI MINH CITY 0.017241379310344827
HONG KONG 0.05172413793103448
ISTANBUL 0.017241379310344827
JEDDAH 0.06896551724137931
JOHANNESBERG 0.008620689655172414
KABUL 0.008620689655172414
KARACHI 0.017241379310344827
KATHMANDU 0.04310344827586207
KUALALUMPUR 0.10344827586206896
KUNMING 0.017241379310344827
KUWAIT 0.09482758620689655
LAHORE 0.008620689655172414
LEIPZIG 0.02586206896551724
LONDON 0.06896551724137931
LUXEMBOURG 0.02586206896551724
MALE 0.06034482758620689
MALPENSA 0.017241379310344827
MANDALAY 0.008620689655172414
MARUITIUS 0.034482758620689655
MEDINA 0.034482758620689655
MELBOURNE 0.008620689655172414
MOSCOW 0.02586206896551724
MUENCHEN 0.017241379310344827
MUSCAT 0.11206896551724138
NAIROBI 0.008620689655172414
NARITA 0.017241379310344827
NAVOI 0.017241379310344827
NEW YORK 0.017241379310344827
NEWARK 0.02586206896551724
OSAKA 0.02586206896551724
PARIS 0.034482758620689655
PARO 0.017241379310344827
PEKING 0.008620689655172414
RANGOON 0.034482758620689655
RIYADH 0.07758620689655173
ROME 0.008620689655172414
SALALAH 0.02586206896551724
SANAA 0.008620689655172414
SEOUL 0.02586206896551724
SEYCHELLES 0.008620689655172414
SHANGHAI 0.017241379310344827
SHARJAH 0.1810344827586207
SHENZHEN 0.008620689655172414
SINGAPORE 0.12068965517241378
SYDNEY 0.008620689655172414
TAIPAE 0.017241379310344827
TASHKENT 0.017241379310344827
TEHRAN 0.017241379310344827
TELAVIV 0.008620689655172414
TOKYO 0.008620689655172414
TORONTO 0.008620689655172414
VIENNA 0.008620689655172414
ZURICH 0.017241379310344827
BAGHDAD 0.008620689655172414
REUNION 0.008620689655172414
DON MUEANG 0.04310344827586207
KANDAHAR 0.008620689655172414
MANCHESTER 0.008620689655172414
SAN FRANCISCO 0.017241379310344827
BASRA 0.008620689655172414
RASALKHAMA 0.017241379310344827
ANTALYA 0.017241379310344827
BAKU 0.008620689655172414
MADRID 0.008620689655172414
MASHHAD 0.008620689655172414
TANGERANG 0.008620689655172414
VANCOUVER 0.008620689655172414

AHMEDABAD 0.08620689655172414
BENGALURU 0.20689655172413793
CHENNAI 0.23275862068965517
DELHI 0.6379310344827586
GOA 0.10344827586206896
HYDERABAD 0.16379310344827586
JAIPUR 0.05172413793103448
KOCHI 0.14655172413793102
KOLKATA 0.14655172413793102
KOZHIKODE 0.11206896551724138
LUCKNOW 0.06896551724137931
MANGALORE 0.06896551724137931
MUMBAI 0.47413793103448276
PUNE 0.034482758620689655
TRIVANDRUM 0.14655172413793102
AMRITSAR 0.08620689655172414
GAYA 0.04310344827586207
VARANASI 0.04310344827586207
MADURAI 0.017241379310344827
TIRUCHIRAPALLY 0.04310344827586207
VISAKHAPATNAM 0.034482758620689655
COIMBATORE 0.017241379310344827
NAGPUR 0.017241379310344827
CHANDIGARH 0.017241379310344827

df1

	year	quarter	city1	city2	passengers_from_city1_to_city2	passengers_from_city2_to_city1	freight_from_city1_to_city2	freight_from_city2_to_city1	pfc1c2_scale	pfc2c1_scale
0	2015	1	ABUDHABI	AHMEDABAD	12764	15358	15.5	64.9	0.041931	0.047518
1	2015	1	ABUDHABI	BENGALURU	27341	35988	372.5	442.1	0.089818	0.111347
2	2015	1	ABUDHABI	CHENNAI	31756	32593	65.8	97.0	0.104322	0.100843
3	2015	1	ABUDHABI	DELHI	66416	65956	1542.3	1683.5	0.218183	0.204068
4	2015	1	ABUDHABI	GOA	8408	12593	18.8	0.0	0.027621	0.038963
...	...	...	...	...	...	...	...	...	...	...
2836	2017	1	TOKYO	DELHI	10276	13292	1259.2	723.8	0.033758	0.041125
2837	2017	1	TORONTO	DELHI	21579	29078	187.3	358.8	0.070889	0.089967
2838	2017	1	VIENNA	DELHI	6363	8275	125.8	87.5	0.020903	0.025603
2839	2017	1	ZURICH	DELHI	14578	20335	1083.9	1429.7	0.047890	0.062917
2840	2017	1	ZURICH	MUMBAI	15487	20426	1205.4	1482.5	0.050876	0.063198

2841 rows × 10 columns

G = nx.Graph()
for index, row in df1.iterrows():
    G.add_edge(row["city1"], row["city2"])

MDG = nx.MultiDiGraph()
#DG2 = nx.DiGraph()
for index, row in df1.iterrows():
    MDG.add_edge(row["city1"], row["city2"], weight=row['pfc1c2_scale'])
    MDG.add_edge(row["city2"], row["city1"], weight=row['pfc2c1_scale'])

DG12 = nx.DiGraph()
DG21 = nx.DiGraph()
#DG2 = nx.DiGraph()
for index, row in df1.iterrows():
    DG12.add_edge(row["city1"], row["city2"], weight=row['pfc1c2_scale'])
    DG21.add_edge(row["city2"], row["city1"], weight=row['pfc2c1_scale'])

plt.figure(figsize=(20, 20), dpi=80)
elarge = [(u, v) for (u, v, d) in MDG.edges(data=True) if d["weight"] > 0.5]
esmall = [(u, v) for (u, v, d) in MDG.edges(data=True) if d["weight"] <= 0.5]

pos = nx.spring_layout(MDG, seed=7)  # positions for all nodes - seed for reproducibility

# nodes
nx.draw_networkx_nodes(MDG, pos, node_size=70)

# edges
nx.draw_networkx_edges(MDG, pos, edgelist=elarge, width=2)
nx.draw_networkx_edges(
    MDG, pos, edgelist=esmall, width=1, alpha=0.5, edge_color="b", style="dashed"
)

# labels
nx.draw_networkx_labels(MDG, pos, font_size=12, font_family="sans-serif")

ax = plt.gca()
ax.margins(0.08)
plt.axis("off")
plt.tight_layout()
plt.show()

nx.in_degree_centrality(MDG)

{'ABUDHABI': 1.1551724137931034,
 'AHMEDABAD': 0.7758620689655172,
 'BENGALURU': 1.7844827586206897,
 'CHENNAI': 2.0,
 'DELHI': 5.310344827586206,
 'GOA': 0.7672413793103449,
 'HYDERABAD': 1.3879310344827587,
 'JAIPUR': 0.33620689655172414,
 'KOCHI': 1.2758620689655171,
 'KOLKATA': 1.1810344827586208,
 'KOZHIKODE': 0.9741379310344828,
 'LUCKNOW': 0.48275862068965514,
 'MANGALORE': 0.5689655172413793,
 'MUMBAI': 3.8448275862068964,
 'PUNE': 0.3103448275862069,
 'TRIVANDRUM': 1.1637931034482758,
 'ADDIS ABABA': 0.22413793103448276,
 'ADEN': 0.07758620689655173,
 'AL AIN': 0.08620689655172414,
 'ALMATY': 0.12931034482758622,
 'AMSTERDAM': 0.13793103448275862,
 'ASHGABAT': 0.15517241379310345,
 'AMRITSAR': 0.6206896551724138,
 'BAHRAIN': 0.6982758620689655,
 'BANGKOK': 0.7327586206896551,
 'GAYA': 0.3189655172413793,
 'VARANASI': 0.3103448275862069,
 'BIRMINGHAM': 0.15517241379310345,
 'BISHKEK': 0.07758620689655173,
 'BRUSELLS': 0.15517241379310345,
 'CAIRO': 0.07758620689655173,
 'CHICAGO': 0.15517241379310345,
 'CHITTAGONG': 0.07758620689655173,
 'COLOGNE': 0.12931034482758622,
 'COLOMBO': 0.8362068965517241,
 'MADURAI': 0.15517241379310345,
 'TIRUCHIRAPALLY': 0.33620689655172414,
 'DAMMAM': 0.6982758620689655,
 'DHAKA': 0.3103448275862069,
 'DOHA': 1.0517241379310345,
 'DUBAI': 1.5,
 'VISAKHAPATNAM': 0.25862068965517243,
 'DUSHANBE': 0.07758620689655173,
 'FRANKFURT': 0.5862068965517241,
 'GUANGZHOU': 0.15517241379310345,
 'HAA DHAALU ATOLL': 0.07758620689655173,
 'HELNSKI': 0.07758620689655173,
 'HO CHI MINH CITY': 0.13793103448275862,
 'HONG KONG': 0.4482758620689655,
 'ISTANBUL': 0.15517241379310345,
 'JEDDAH': 0.6206896551724138,
 'JOHANNESBERG': 0.008620689655172414,
 'KABUL': 0.07758620689655173,
 'KARACHI': 0.15517241379310345,
 'KATHMANDU': 0.3706896551724138,
 'KUALALUMPUR': 0.8706896551724138,
 'KUNMING': 0.12931034482758622,
 'KUWAIT': 0.8362068965517241,
 'LAHORE': 0.07758620689655173,
 'LEIPZIG': 0.1896551724137931,
 'LONDON': 0.5948275862068966,
 'LUXEMBOURG': 0.1896551724137931,
 'MALE': 0.35344827586206895,
 'MALPENSA': 0.12068965517241378,
 'MANDALAY': 0.07758620689655173,
 'MARUITIUS': 0.3103448275862069,
 'MEDINA': 0.28448275862068967,
 'MELBOURNE': 0.07758620689655173,
 'MOSCOW': 0.1724137931034483,
 'MUENCHEN': 0.15517241379310345,
 'MUSCAT': 1.0086206896551724,
 'NAIROBI': 0.07758620689655173,
 'NARITA': 0.15517241379310345,
 'NAVOI': 0.14655172413793102,
 'NEW YORK': 0.15517241379310345,
 'NEWARK': 0.23275862068965517,
 'OSAKA': 0.19827586206896552,
 'PARIS': 0.3103448275862069,
 'PARO': 0.15517241379310345,
 'PEKING': 0.07758620689655173,
 'RANGOON': 0.25,
 'RIYADH': 0.6982758620689655,
 'ROME': 0.07758620689655173,
 'SALALAH': 0.23275862068965517,
 'SANAA': 0.07758620689655173,
 'SEOUL': 0.23275862068965517,
 'SEYCHELLES': 0.07758620689655173,
 'SHANGHAI': 0.15517241379310345,
 'SHARJAH': 1.3017241379310345,
 'COIMBATORE': 0.15517241379310345,
 'NAGPUR': 0.12068965517241378,
 'SHENZHEN': 0.07758620689655173,
 'SINGAPORE': 1.0172413793103448,
 'SYDNEY': 0.07758620689655173,
 'TAIPAE': 0.15517241379310345,
 'TASHKENT': 0.15517241379310345,
 'TEHRAN': 0.15517241379310345,
 'TELAVIV': 0.07758620689655173,
 'TOKYO': 0.07758620689655173,
 'TORONTO': 0.07758620689655173,
 'VIENNA': 0.07758620689655173,
 'ZURICH': 0.15517241379310345,
 'BAGHDAD': 0.06896551724137931,
 'REUNION': 0.06896551724137931,
 'DON MUEANG': 0.1810344827586207,
 'KANDAHAR': 0.05172413793103448,
 'MANCHESTER': 0.05172413793103448,
 'SAN FRANCISCO': 0.10344827586206896,
 'BASRA': 0.04310344827586207,
 'RASALKHAMA': 0.05172413793103448,
 'ANTALYA': 0.06896551724137931,
 'BAKU': 0.008620689655172414,
 'CHANDIGARH': 0.05172413793103448,
 'MADRID': 0.017241379310344827,
 'MASHHAD': 0.017241379310344827,
 'TANGERANG': 0.008620689655172414,
 'VANCOUVER': 0.017241379310344827}

nx.out_degree_centrality(MDG)

{'ABUDHABI': 1.1551724137931034,
 'AHMEDABAD': 0.7758620689655172,
 'BENGALURU': 1.7844827586206897,
 'CHENNAI': 2.0,
 'DELHI': 5.310344827586206,
 'GOA': 0.7672413793103449,
 'HYDERABAD': 1.3879310344827587,
 'JAIPUR': 0.33620689655172414,
 'KOCHI': 1.2758620689655171,
 'KOLKATA': 1.1810344827586208,
 'KOZHIKODE': 0.9741379310344828,
 'LUCKNOW': 0.48275862068965514,
 'MANGALORE': 0.5689655172413793,
 'MUMBAI': 3.8448275862068964,
 'PUNE': 0.3103448275862069,
 'TRIVANDRUM': 1.1637931034482758,
 'ADDIS ABABA': 0.22413793103448276,
 'ADEN': 0.07758620689655173,
 'AL AIN': 0.08620689655172414,
 'ALMATY': 0.12931034482758622,
 'AMSTERDAM': 0.13793103448275862,
 'ASHGABAT': 0.15517241379310345,
 'AMRITSAR': 0.6206896551724138,
 'BAHRAIN': 0.6982758620689655,
 'BANGKOK': 0.7327586206896551,
 'GAYA': 0.3189655172413793,
 'VARANASI': 0.3103448275862069,
 'BIRMINGHAM': 0.15517241379310345,
 'BISHKEK': 0.07758620689655173,
 'BRUSELLS': 0.15517241379310345,
 'CAIRO': 0.07758620689655173,
 'CHICAGO': 0.15517241379310345,
 'CHITTAGONG': 0.07758620689655173,
 'COLOGNE': 0.12931034482758622,
 'COLOMBO': 0.8362068965517241,
 'MADURAI': 0.15517241379310345,
 'TIRUCHIRAPALLY': 0.33620689655172414,
 'DAMMAM': 0.6982758620689655,
 'DHAKA': 0.3103448275862069,
 'DOHA': 1.0517241379310345,
 'DUBAI': 1.5,
 'VISAKHAPATNAM': 0.25862068965517243,
 'DUSHANBE': 0.07758620689655173,
 'FRANKFURT': 0.5862068965517241,
 'GUANGZHOU': 0.15517241379310345,
 'HAA DHAALU ATOLL': 0.07758620689655173,
 'HELNSKI': 0.07758620689655173,
 'HO CHI MINH CITY': 0.13793103448275862,
 'HONG KONG': 0.4482758620689655,
 'ISTANBUL': 0.15517241379310345,
 'JEDDAH': 0.6206896551724138,
 'JOHANNESBERG': 0.008620689655172414,
 'KABUL': 0.07758620689655173,
 'KARACHI': 0.15517241379310345,
 'KATHMANDU': 0.3706896551724138,
 'KUALALUMPUR': 0.8706896551724138,
 'KUNMING': 0.12931034482758622,
 'KUWAIT': 0.8362068965517241,
 'LAHORE': 0.07758620689655173,
 'LEIPZIG': 0.1896551724137931,
 'LONDON': 0.5948275862068966,
 'LUXEMBOURG': 0.1896551724137931,
 'MALE': 0.35344827586206895,
 'MALPENSA': 0.12068965517241378,
 'MANDALAY': 0.07758620689655173,
 'MARUITIUS': 0.3103448275862069,
 'MEDINA': 0.28448275862068967,
 'MELBOURNE': 0.07758620689655173,
 'MOSCOW': 0.1724137931034483,
 'MUENCHEN': 0.15517241379310345,
 'MUSCAT': 1.0086206896551724,
 'NAIROBI': 0.07758620689655173,
 'NARITA': 0.15517241379310345,
 'NAVOI': 0.14655172413793102,
 'NEW YORK': 0.15517241379310345,
 'NEWARK': 0.23275862068965517,
 'OSAKA': 0.19827586206896552,
 'PARIS': 0.3103448275862069,
 'PARO': 0.15517241379310345,
 'PEKING': 0.07758620689655173,
 'RANGOON': 0.25,
 'RIYADH': 0.6982758620689655,
 'ROME': 0.07758620689655173,
 'SALALAH': 0.23275862068965517,
 'SANAA': 0.07758620689655173,
 'SEOUL': 0.23275862068965517,
 'SEYCHELLES': 0.07758620689655173,
 'SHANGHAI': 0.15517241379310345,
 'SHARJAH': 1.3017241379310345,
 'COIMBATORE': 0.15517241379310345,
 'NAGPUR': 0.12068965517241378,
 'SHENZHEN': 0.07758620689655173,
 'SINGAPORE': 1.0172413793103448,
 'SYDNEY': 0.07758620689655173,
 'TAIPAE': 0.15517241379310345,
 'TASHKENT': 0.15517241379310345,
 'TEHRAN': 0.15517241379310345,
 'TELAVIV': 0.07758620689655173,
 'TOKYO': 0.07758620689655173,
 'TORONTO': 0.07758620689655173,
 'VIENNA': 0.07758620689655173,
 'ZURICH': 0.15517241379310345,
 'BAGHDAD': 0.06896551724137931,
 'REUNION': 0.06896551724137931,
 'DON MUEANG': 0.1810344827586207,
 'KANDAHAR': 0.05172413793103448,
 'MANCHESTER': 0.05172413793103448,
 'SAN FRANCISCO': 0.10344827586206896,
 'BASRA': 0.04310344827586207,
 'RASALKHAMA': 0.05172413793103448,
 'ANTALYA': 0.06896551724137931,
 'BAKU': 0.008620689655172414,
 'CHANDIGARH': 0.05172413793103448,
 'MADRID': 0.017241379310344827,
 'MASHHAD': 0.017241379310344827,
 'TANGERANG': 0.008620689655172414,
 'VANCOUVER': 0.017241379310344827}

MDG.nodes

NodeView(('ABUDHABI', 'AHMEDABAD', 'BENGALURU', 'CHENNAI', 'DELHI', 'GOA', 'HYDERABAD', 'JAIPUR', 'KOCHI', 'KOLKATA', 'KOZHIKODE', 'LUCKNOW', 'MANGALORE', 'MUMBAI', 'PUNE', 'TRIVANDRUM', 'ADDIS ABABA', 'ADEN', 'AL AIN', 'ALMATY', 'AMSTERDAM', 'ASHGABAT', 'AMRITSAR', 'BAHRAIN', 'BANGKOK', 'GAYA', 'VARANASI', 'BIRMINGHAM', 'BISHKEK', 'BRUSELLS', 'CAIRO', 'CHICAGO', 'CHITTAGONG', 'COLOGNE', 'COLOMBO', 'MADURAI', 'TIRUCHIRAPALLY', 'DAMMAM', 'DHAKA', 'DOHA', 'DUBAI', 'VISAKHAPATNAM', 'DUSHANBE', 'FRANKFURT', 'GUANGZHOU', 'HAA DHAALU ATOLL', 'HELNSKI', 'HO CHI MINH CITY', 'HONG KONG', 'ISTANBUL', 'JEDDAH', 'JOHANNESBERG', 'KABUL', 'KARACHI', 'KATHMANDU', 'KUALALUMPUR', 'KUNMING', 'KUWAIT', 'LAHORE', 'LEIPZIG', 'LONDON', 'LUXEMBOURG', 'MALE', 'MALPENSA', 'MANDALAY', 'MARUITIUS', 'MEDINA', 'MELBOURNE', 'MOSCOW', 'MUENCHEN', 'MUSCAT', 'NAIROBI', 'NARITA', 'NAVOI', 'NEW YORK', 'NEWARK', 'OSAKA', 'PARIS', 'PARO', 'PEKING', 'RANGOON', 'RIYADH', 'ROME', 'SALALAH', 'SANAA', 'SEOUL', 'SEYCHELLES', 'SHANGHAI', 'SHARJAH', 'COIMBATORE', 'NAGPUR', 'SHENZHEN', 'SINGAPORE', 'SYDNEY', 'TAIPAE', 'TASHKENT', 'TEHRAN', 'TELAVIV', 'TOKYO', 'TORONTO', 'VIENNA', 'ZURICH', 'BAGHDAD', 'REUNION', 'DON MUEANG', 'KANDAHAR', 'MANCHESTER', 'SAN FRANCISCO', 'BASRA', 'RASALKHAMA', 'ANTALYA', 'BAKU', 'CHANDIGARH', 'MADRID', 'MASHHAD', 'TANGERANG', 'VANCOUVER'))

MDG.in_edges

InMultiEdgeView([('AHMEDABAD', 'ABUDHABI', 0), ('AHMEDABAD', 'ABUDHABI', 1), ('AHMEDABAD', 'ABUDHABI', 2), ('AHMEDABAD', 'ABUDHABI', 3), ('AHMEDABAD', 'ABUDHABI', 4), ('AHMEDABAD', 'ABUDHABI', 5), ('AHMEDABAD', 'ABUDHABI', 6), ('AHMEDABAD', 'ABUDHABI', 7), ('AHMEDABAD', 'ABUDHABI', 8), ('BENGALURU', 'ABUDHABI', 0), ('BENGALURU', 'ABUDHABI', 1), ('BENGALURU', 'ABUDHABI', 2), ('BENGALURU', 'ABUDHABI', 3), ('BENGALURU', 'ABUDHABI', 4), ('BENGALURU', 'ABUDHABI', 5), ('BENGALURU', 'ABUDHABI', 6), ('BENGALURU', 'ABUDHABI', 7), ('BENGALURU', 'ABUDHABI', 8), ('CHENNAI', 'ABUDHABI', 0), ('CHENNAI', 'ABUDHABI', 1), ('CHENNAI', 'ABUDHABI', 2), ('CHENNAI', 'ABUDHABI', 3), ('CHENNAI', 'ABUDHABI', 4), ('CHENNAI', 'ABUDHABI', 5), ('CHENNAI', 'ABUDHABI', 6), ('CHENNAI', 'ABUDHABI', 7), ('CHENNAI', 'ABUDHABI', 8), ('DELHI', 'ABUDHABI', 0), ('DELHI', 'ABUDHABI', 1), ('DELHI', 'ABUDHABI', 2), ('DELHI', 'ABUDHABI', 3), ('DELHI', 'ABUDHABI', 4), ('DELHI', 'ABUDHABI', 5), ('DELHI', 'ABUDHABI', 6), ('DELHI', 'ABUDHABI', 7), ('DELHI', 'ABUDHABI', 8), ('GOA', 'ABUDHABI', 0), ('GOA', 'ABUDHABI', 1), ('GOA', 'ABUDHABI', 2), ('GOA', 'ABUDHABI', 3), ('GOA', 'ABUDHABI', 4), ('GOA', 'ABUDHABI', 5), ('GOA', 'ABUDHABI', 6), ('GOA', 'ABUDHABI', 7), ('GOA', 'ABUDHABI', 8), ('HYDERABAD', 'ABUDHABI', 0), ('HYDERABAD', 'ABUDHABI', 1), ('HYDERABAD', 'ABUDHABI', 2), ('HYDERABAD', 'ABUDHABI', 3), ('HYDERABAD', 'ABUDHABI', 4), ('HYDERABAD', 'ABUDHABI', 5), ('HYDERABAD', 'ABUDHABI', 6), ('HYDERABAD', 'ABUDHABI', 7), ('HYDERABAD', 'ABUDHABI', 8), ('JAIPUR', 'ABUDHABI', 0), ('JAIPUR', 'ABUDHABI', 1), ('JAIPUR', 'ABUDHABI', 2), ('JAIPUR', 'ABUDHABI', 3), ('JAIPUR', 'ABUDHABI', 4), ('JAIPUR', 'ABUDHABI', 5), ('JAIPUR', 'ABUDHABI', 6), ('JAIPUR', 'ABUDHABI', 7), ('JAIPUR', 'ABUDHABI', 8), ('KOCHI', 'ABUDHABI', 0), ('KOCHI', 'ABUDHABI', 1), ('KOCHI', 'ABUDHABI', 2), ('KOCHI', 'ABUDHABI', 3), ('KOCHI', 'ABUDHABI', 4), ('KOCHI', 'ABUDHABI', 5), ('KOCHI', 'ABUDHABI', 6), ('KOCHI', 'ABUDHABI', 7), ('KOCHI', 'ABUDHABI', 8), ('KOLKATA', 'ABUDHABI', 0), ('KOLKATA', 'ABUDHABI', 1), ('KOLKATA', 'ABUDHABI', 2), ('KOLKATA', 'ABUDHABI', 3), ('KOLKATA', 'ABUDHABI', 4), ('KOLKATA', 'ABUDHABI', 5), ('KOLKATA', 'ABUDHABI', 6), ('KOLKATA', 'ABUDHABI', 7), ('KOZHIKODE', 'ABUDHABI', 0), ('KOZHIKODE', 'ABUDHABI', 1), ('KOZHIKODE', 'ABUDHABI', 2), ('KOZHIKODE', 'ABUDHABI', 3), ('KOZHIKODE', 'ABUDHABI', 4), ('KOZHIKODE', 'ABUDHABI', 5), ('KOZHIKODE', 'ABUDHABI', 6), ('KOZHIKODE', 'ABUDHABI', 7), ('KOZHIKODE', 'ABUDHABI', 8), ('LUCKNOW', 'ABUDHABI', 0), ('LUCKNOW', 'ABUDHABI', 1), ('LUCKNOW', 'ABUDHABI', 2), ('LUCKNOW', 'ABUDHABI', 3), ('LUCKNOW', 'ABUDHABI', 4), ('LUCKNOW', 'ABUDHABI', 5), ('LUCKNOW', 'ABUDHABI', 6), ('LUCKNOW', 'ABUDHABI', 7), ('LUCKNOW', 'ABUDHABI', 8), ('MANGALORE', 'ABUDHABI', 0), ('MANGALORE', 'ABUDHABI', 1), ('MANGALORE', 'ABUDHABI', 2), ('MANGALORE', 'ABUDHABI', 3), ('MANGALORE', 'ABUDHABI', 4), ('MANGALORE', 'ABUDHABI', 5), ('MANGALORE', 'ABUDHABI', 6), ('MANGALORE', 'ABUDHABI', 7), 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('DOHA', 'NAGPUR', 0), ('DOHA', 'NAGPUR', 1), ('DOHA', 'NAGPUR', 2), ('DOHA', 'NAGPUR', 3), ('DOHA', 'NAGPUR', 4), ('MUMBAI', 'SHENZHEN', 0), ('MUMBAI', 'SHENZHEN', 1), ('MUMBAI', 'SHENZHEN', 2), ('MUMBAI', 'SHENZHEN', 3), ('MUMBAI', 'SHENZHEN', 4), ('MUMBAI', 'SHENZHEN', 5), ('MUMBAI', 'SHENZHEN', 6), ('MUMBAI', 'SHENZHEN', 7), ('MUMBAI', 'SHENZHEN', 8), ('AHMEDABAD', 'SINGAPORE', 0), ('AHMEDABAD', 'SINGAPORE', 1), ('AHMEDABAD', 'SINGAPORE', 2), ('AHMEDABAD', 'SINGAPORE', 3), ('AHMEDABAD', 'SINGAPORE', 4), ('AHMEDABAD', 'SINGAPORE', 5), ('AHMEDABAD', 'SINGAPORE', 6), ('AHMEDABAD', 'SINGAPORE', 7), ('AHMEDABAD', 'SINGAPORE', 8), ('BENGALURU', 'SINGAPORE', 0), ('BENGALURU', 'SINGAPORE', 1), ('BENGALURU', 'SINGAPORE', 2), ('BENGALURU', 'SINGAPORE', 3), ('BENGALURU', 'SINGAPORE', 4), ('BENGALURU', 'SINGAPORE', 5), ('BENGALURU', 'SINGAPORE', 6), ('BENGALURU', 'SINGAPORE', 7), ('BENGALURU', 'SINGAPORE', 8), ('CHENNAI', 'SINGAPORE', 0), ('CHENNAI', 'SINGAPORE', 1), ('CHENNAI', 'SINGAPORE', 2), ('CHENNAI', 'SINGAPORE', 3), ('CHENNAI', 'SINGAPORE', 4), ('CHENNAI', 'SINGAPORE', 5), ('CHENNAI', 'SINGAPORE', 6), ('CHENNAI', 'SINGAPORE', 7), ('CHENNAI', 'SINGAPORE', 8), ('COIMBATORE', 'SINGAPORE', 0), ('COIMBATORE', 'SINGAPORE', 1), ('COIMBATORE', 'SINGAPORE', 2), ('COIMBATORE', 'SINGAPORE', 3), ('COIMBATORE', 'SINGAPORE', 4), ('COIMBATORE', 'SINGAPORE', 5), ('COIMBATORE', 'SINGAPORE', 6), ('COIMBATORE', 'SINGAPORE', 7), ('COIMBATORE', 'SINGAPORE', 8), ('DELHI', 'SINGAPORE', 0), ('DELHI', 'SINGAPORE', 1), ('DELHI', 'SINGAPORE', 2), ('DELHI', 'SINGAPORE', 3), ('DELHI', 'SINGAPORE', 4), ('DELHI', 'SINGAPORE', 5), ('DELHI', 'SINGAPORE', 6), ('DELHI', 'SINGAPORE', 7), ('DELHI', 'SINGAPORE', 8), ('HYDERABAD', 'SINGAPORE', 0), ('HYDERABAD', 'SINGAPORE', 1), ('HYDERABAD', 'SINGAPORE', 2), ('HYDERABAD', 'SINGAPORE', 3), ('HYDERABAD', 'SINGAPORE', 4), ('HYDERABAD', 'SINGAPORE', 5), ('HYDERABAD', 'SINGAPORE', 6), ('HYDERABAD', 'SINGAPORE', 7), ('HYDERABAD', 'SINGAPORE', 8), ('KOCHI', 'SINGAPORE', 0), ('KOCHI', 'SINGAPORE', 1), ('KOCHI', 'SINGAPORE', 2), ('KOCHI', 'SINGAPORE', 3), ('KOCHI', 'SINGAPORE', 4), ('KOCHI', 'SINGAPORE', 5), ('KOCHI', 'SINGAPORE', 6), ('KOCHI', 'SINGAPORE', 7), ('KOCHI', 'SINGAPORE', 8), ('KOLKATA', 'SINGAPORE', 0), ('KOLKATA', 'SINGAPORE', 1), ('KOLKATA', 'SINGAPORE', 2), ('KOLKATA', 'SINGAPORE', 3), ('KOLKATA', 'SINGAPORE', 4), ('KOLKATA', 'SINGAPORE', 5), ('KOLKATA', 'SINGAPORE', 6), ('KOLKATA', 'SINGAPORE', 7), ('KOLKATA', 'SINGAPORE', 8), ('MUMBAI', 'SINGAPORE', 0), ('MUMBAI', 'SINGAPORE', 1), ('MUMBAI', 'SINGAPORE', 2), ('MUMBAI', 'SINGAPORE', 3), ('MUMBAI', 'SINGAPORE', 4), ('MUMBAI', 'SINGAPORE', 5), ('MUMBAI', 'SINGAPORE', 6), ('MUMBAI', 'SINGAPORE', 7), ('MUMBAI', 'SINGAPORE', 8), ('TIRUCHIRAPALLY', 'SINGAPORE', 0), ('TIRUCHIRAPALLY', 'SINGAPORE', 1), ('TIRUCHIRAPALLY', 'SINGAPORE', 2), ('TIRUCHIRAPALLY', 'SINGAPORE', 3), ('TIRUCHIRAPALLY', 'SINGAPORE', 4), ('TIRUCHIRAPALLY', 'SINGAPORE', 5), ('TIRUCHIRAPALLY', 'SINGAPORE', 6), ('TIRUCHIRAPALLY', 'SINGAPORE', 7), ('TIRUCHIRAPALLY', 'SINGAPORE', 8), ('TRIVANDRUM', 'SINGAPORE', 0), ('TRIVANDRUM', 'SINGAPORE', 1), ('TRIVANDRUM', 'SINGAPORE', 2), ('TRIVANDRUM', 'SINGAPORE', 3), ('TRIVANDRUM', 'SINGAPORE', 4), ('TRIVANDRUM', 'SINGAPORE', 5), ('TRIVANDRUM', 'SINGAPORE', 6), ('TRIVANDRUM', 'SINGAPORE', 7), ('TRIVANDRUM', 'SINGAPORE', 8), ('VISAKHAPATNAM', 'SINGAPORE', 0), ('VISAKHAPATNAM', 'SINGAPORE', 1), ('VISAKHAPATNAM', 'SINGAPORE', 2), ('VISAKHAPATNAM', 'SINGAPORE', 3), ('VISAKHAPATNAM', 'SINGAPORE', 4), ('VISAKHAPATNAM', 'SINGAPORE', 5), ('VISAKHAPATNAM', 'SINGAPORE', 6), ('VISAKHAPATNAM', 'SINGAPORE', 7), ('VISAKHAPATNAM', 'SINGAPORE', 8), ('LUCKNOW', 'SINGAPORE', 0), ('LUCKNOW', 'SINGAPORE', 1), ('LUCKNOW', 'SINGAPORE', 2), ('LUCKNOW', 'SINGAPORE', 3), ('LUCKNOW', 'SINGAPORE', 4), ('LUCKNOW', 'SINGAPORE', 5), ('AMRITSAR', 'SINGAPORE', 0), ('AMRITSAR', 'SINGAPORE', 1), ('AMRITSAR', 'SINGAPORE', 2), ('AMRITSAR', 'SINGAPORE', 3), ('DELHI', 'SYDNEY', 0), ('DELHI', 'SYDNEY', 1), ('DELHI', 'SYDNEY', 2), ('DELHI', 'SYDNEY', 3), ('DELHI', 'SYDNEY', 4), ('DELHI', 'SYDNEY', 5), ('DELHI', 'SYDNEY', 6), ('DELHI', 'SYDNEY', 7), ('DELHI', 'SYDNEY', 8), ('DELHI', 'TAIPAE', 0), ('DELHI', 'TAIPAE', 1), ('DELHI', 'TAIPAE', 2), ('DELHI', 'TAIPAE', 3), ('DELHI', 'TAIPAE', 4), ('DELHI', 'TAIPAE', 5), ('DELHI', 'TAIPAE', 6), ('DELHI', 'TAIPAE', 7), ('DELHI', 'TAIPAE', 8), ('MUMBAI', 'TAIPAE', 0), ('MUMBAI', 'TAIPAE', 1), ('MUMBAI', 'TAIPAE', 2), ('MUMBAI', 'TAIPAE', 3), ('MUMBAI', 'TAIPAE', 4), ('MUMBAI', 'TAIPAE', 5), ('MUMBAI', 'TAIPAE', 6), ('MUMBAI', 'TAIPAE', 7), ('MUMBAI', 'TAIPAE', 8), ('AMRITSAR', 'TASHKENT', 0), ('AMRITSAR', 'TASHKENT', 1), ('AMRITSAR', 'TASHKENT', 2), ('AMRITSAR', 'TASHKENT', 3), ('AMRITSAR', 'TASHKENT', 4), ('AMRITSAR', 'TASHKENT', 5), ('AMRITSAR', 'TASHKENT', 6), ('AMRITSAR', 'TASHKENT', 7), ('AMRITSAR', 'TASHKENT', 8), ('DELHI', 'TASHKENT', 0), ('DELHI', 'TASHKENT', 1), ('DELHI', 'TASHKENT', 2), ('DELHI', 'TASHKENT', 3), ('DELHI', 'TASHKENT', 4), ('DELHI', 'TASHKENT', 5), ('DELHI', 'TASHKENT', 6), ('DELHI', 'TASHKENT', 7), ('DELHI', 'TASHKENT', 8), ('DELHI', 'TEHRAN', 0), ('DELHI', 'TEHRAN', 1), ('DELHI', 'TEHRAN', 2), ('DELHI', 'TEHRAN', 3), ('DELHI', 'TEHRAN', 4), ('DELHI', 'TEHRAN', 5), ('DELHI', 'TEHRAN', 6), ('DELHI', 'TEHRAN', 7), ('DELHI', 'TEHRAN', 8), ('MUMBAI', 'TEHRAN', 0), ('MUMBAI', 'TEHRAN', 1), ('MUMBAI', 'TEHRAN', 2), ('MUMBAI', 'TEHRAN', 3), ('MUMBAI', 'TEHRAN', 4), ('MUMBAI', 'TEHRAN', 5), ('MUMBAI', 'TEHRAN', 6), ('MUMBAI', 'TEHRAN', 7), ('MUMBAI', 'TEHRAN', 8), ('MUMBAI', 'TELAVIV', 0), ('MUMBAI', 'TELAVIV', 1), ('MUMBAI', 'TELAVIV', 2), ('MUMBAI', 'TELAVIV', 3), ('MUMBAI', 'TELAVIV', 4), ('MUMBAI', 'TELAVIV', 5), ('MUMBAI', 'TELAVIV', 6), ('MUMBAI', 'TELAVIV', 7), ('MUMBAI', 'TELAVIV', 8), ('DELHI', 'TOKYO', 0), ('DELHI', 'TOKYO', 1), ('DELHI', 'TOKYO', 2), ('DELHI', 'TOKYO', 3), ('DELHI', 'TOKYO', 4), ('DELHI', 'TOKYO', 5), ('DELHI', 'TOKYO', 6), ('DELHI', 'TOKYO', 7), ('DELHI', 'TOKYO', 8), ('DELHI', 'TORONTO', 0), ('DELHI', 'TORONTO', 1), ('DELHI', 'TORONTO', 2), ('DELHI', 'TORONTO', 3), ('DELHI', 'TORONTO', 4), ('DELHI', 'TORONTO', 5), ('DELHI', 'TORONTO', 6), ('DELHI', 'TORONTO', 7), ('DELHI', 'TORONTO', 8), ('DELHI', 'VIENNA', 0), ('DELHI', 'VIENNA', 1), ('DELHI', 'VIENNA', 2), ('DELHI', 'VIENNA', 3), ('DELHI', 'VIENNA', 4), ('DELHI', 'VIENNA', 5), ('DELHI', 'VIENNA', 6), ('DELHI', 'VIENNA', 7), ('DELHI', 'VIENNA', 8), ('DELHI', 'ZURICH', 0), ('DELHI', 'ZURICH', 1), ('DELHI', 'ZURICH', 2), ('DELHI', 'ZURICH', 3), ('DELHI', 'ZURICH', 4), ('DELHI', 'ZURICH', 5), ('DELHI', 'ZURICH', 6), ('DELHI', 'ZURICH', 7), ('DELHI', 'ZURICH', 8), ('MUMBAI', 'ZURICH', 0), ('MUMBAI', 'ZURICH', 1), ('MUMBAI', 'ZURICH', 2), ('MUMBAI', 'ZURICH', 3), ('MUMBAI', 'ZURICH', 4), ('MUMBAI', 'ZURICH', 5), ('MUMBAI', 'ZURICH', 6), ('MUMBAI', 'ZURICH', 7), ('MUMBAI', 'ZURICH', 8), ('DELHI', 'BAGHDAD', 0), ('DELHI', 'BAGHDAD', 1), ('DELHI', 'BAGHDAD', 2), ('DELHI', 'BAGHDAD', 3), ('DELHI', 'BAGHDAD', 4), ('DELHI', 'BAGHDAD', 5), ('DELHI', 'BAGHDAD', 6), ('DELHI', 'BAGHDAD', 7), ('CHENNAI', 'REUNION', 0), ('CHENNAI', 'REUNION', 1), ('CHENNAI', 'REUNION', 2), ('CHENNAI', 'REUNION', 3), ('CHENNAI', 'REUNION', 4), ('CHENNAI', 'REUNION', 5), ('CHENNAI', 'REUNION', 6), ('CHENNAI', 'REUNION', 7), ('BENGALURU', 'DON MUEANG', 0), ('BENGALURU', 'DON MUEANG', 1), ('BENGALURU', 'DON MUEANG', 2), ('BENGALURU', 'DON MUEANG', 3), ('BENGALURU', 'DON MUEANG', 4), ('BENGALURU', 'DON MUEANG', 5), ('BENGALURU', 'DON MUEANG', 6), ('CHENNAI', 'DON MUEANG', 0), ('CHENNAI', 'DON MUEANG', 1), ('CHENNAI', 'DON MUEANG', 2), ('CHENNAI', 'DON MUEANG', 3), ('CHENNAI', 'DON MUEANG', 4), ('CHENNAI', 'DON MUEANG', 5), ('CHENNAI', 'DON MUEANG', 6), ('GAYA', 'DON MUEANG', 0), ('KOCHI', 'DON MUEANG', 0), ('KOCHI', 'DON MUEANG', 1), ('KOCHI', 'DON MUEANG', 2), ('KOCHI', 'DON MUEANG', 3), ('KOLKATA', 'DON MUEANG', 0), ('KOLKATA', 'DON MUEANG', 1), ('DELHI', 'KANDAHAR', 0), ('DELHI', 'KANDAHAR', 1), ('DELHI', 'KANDAHAR', 2), ('DELHI', 'KANDAHAR', 3), ('DELHI', 'KANDAHAR', 4), ('DELHI', 'KANDAHAR', 5), ('GOA', 'MANCHESTER', 0), ('GOA', 'MANCHESTER', 1), ('GOA', 'MANCHESTER', 2), ('GOA', 'MANCHESTER', 3), ('GOA', 'MANCHESTER', 4), ('GOA', 'MANCHESTER', 5), ('BENGALURU', 'SAN FRANCISCO', 0), ('BENGALURU', 'SAN FRANCISCO', 1), ('BENGALURU', 'SAN FRANCISCO', 2), ('BENGALURU', 'SAN FRANCISCO', 3), ('BENGALURU', 'SAN FRANCISCO', 4), ('BENGALURU', 'SAN FRANCISCO', 5), ('DELHI', 'SAN FRANCISCO', 0), ('DELHI', 'SAN FRANCISCO', 1), ('DELHI', 'SAN FRANCISCO', 2), ('DELHI', 'SAN FRANCISCO', 3), ('DELHI', 'SAN FRANCISCO', 4), ('DELHI', 'SAN FRANCISCO', 5), ('DELHI', 'BASRA', 0), ('DELHI', 'BASRA', 1), ('DELHI', 'BASRA', 2), ('DELHI', 'BASRA', 3), ('DELHI', 'BASRA', 4), ('KOZHIKODE', 'RASALKHAMA', 0), ('KOZHIKODE', 'RASALKHAMA', 1), ('KOZHIKODE', 'RASALKHAMA', 2), ('KOZHIKODE', 'RASALKHAMA', 3), ('KOZHIKODE', 'RASALKHAMA', 4), ('TRIVANDRUM', 'RASALKHAMA', 0), ('DELHI', 'ANTALYA', 0), ('DELHI', 'ANTALYA', 1), ('DELHI', 'ANTALYA', 2), ('DELHI', 'ANTALYA', 3), ('MUMBAI', 'ANTALYA', 0), ('MUMBAI', 'ANTALYA', 1), ('MUMBAI', 'ANTALYA', 2), ('MUMBAI', 'ANTALYA', 3), ('GOA', 'BAKU', 0), ('DUBAI', 'CHANDIGARH', 0), ('DUBAI', 'CHANDIGARH', 1), ('DUBAI', 'CHANDIGARH', 2), ('SHARJAH', 'CHANDIGARH', 0), ('SHARJAH', 'CHANDIGARH', 1), ('SHARJAH', 'CHANDIGARH', 2), ('DELHI', 'MADRID', 0), ('DELHI', 'MADRID', 1), ('DELHI', 'MASHHAD', 0), ('DELHI', 'MASHHAD', 1), ('MUMBAI', 'TANGERANG', 0), ('DELHI', 'VANCOUVER', 0), ('DELHI', 'VANCOUVER', 1)])

MDG.out_edges

OutMultiEdgeView([('ABUDHABI', 'AHMEDABAD', 0), ('ABUDHABI', 'AHMEDABAD', 1), ('ABUDHABI', 'AHMEDABAD', 2), ('ABUDHABI', 'AHMEDABAD', 3), ('ABUDHABI', 'AHMEDABAD', 4), ('ABUDHABI', 'AHMEDABAD', 5), ('ABUDHABI', 'AHMEDABAD', 6), ('ABUDHABI', 'AHMEDABAD', 7), ('ABUDHABI', 'AHMEDABAD', 8), ('ABUDHABI', 'BENGALURU', 0), ('ABUDHABI', 'BENGALURU', 1), ('ABUDHABI', 'BENGALURU', 2), ('ABUDHABI', 'BENGALURU', 3), ('ABUDHABI', 'BENGALURU', 4), ('ABUDHABI', 'BENGALURU', 5), ('ABUDHABI', 'BENGALURU', 6), ('ABUDHABI', 'BENGALURU', 7), ('ABUDHABI', 'BENGALURU', 8), ('ABUDHABI', 'CHENNAI', 0), ('ABUDHABI', 'CHENNAI', 1), ('ABUDHABI', 'CHENNAI', 2), ('ABUDHABI', 'CHENNAI', 3), ('ABUDHABI', 'CHENNAI', 4), ('ABUDHABI', 'CHENNAI', 5), ('ABUDHABI', 'CHENNAI', 6), ('ABUDHABI', 'CHENNAI', 7), ('ABUDHABI', 'CHENNAI', 8), ('ABUDHABI', 'DELHI', 0), ('ABUDHABI', 'DELHI', 1), ('ABUDHABI', 'DELHI', 2), ('ABUDHABI', 'DELHI', 3), ('ABUDHABI', 'DELHI', 4), ('ABUDHABI', 'DELHI', 5), ('ABUDHABI', 'DELHI', 6), ('ABUDHABI', 'DELHI', 7), ('ABUDHABI', 'DELHI', 8), ('ABUDHABI', 'GOA', 0), ('ABUDHABI', 'GOA', 1), ('ABUDHABI', 'GOA', 2), ('ABUDHABI', 'GOA', 3), ('ABUDHABI', 'GOA', 4), ('ABUDHABI', 'GOA', 5), ('ABUDHABI', 'GOA', 6), ('ABUDHABI', 'GOA', 7), ('ABUDHABI', 'GOA', 8), ('ABUDHABI', 'HYDERABAD', 0), ('ABUDHABI', 'HYDERABAD', 1), ('ABUDHABI', 'HYDERABAD', 2), ('ABUDHABI', 'HYDERABAD', 3), ('ABUDHABI', 'HYDERABAD', 4), ('ABUDHABI', 'HYDERABAD', 5), ('ABUDHABI', 'HYDERABAD', 6), ('ABUDHABI', 'HYDERABAD', 7), ('ABUDHABI', 'HYDERABAD', 8), ('ABUDHABI', 'JAIPUR', 0), ('ABUDHABI', 'JAIPUR', 1), ('ABUDHABI', 'JAIPUR', 2), ('ABUDHABI', 'JAIPUR', 3), ('ABUDHABI', 'JAIPUR', 4), ('ABUDHABI', 'JAIPUR', 5), ('ABUDHABI', 'JAIPUR', 6), ('ABUDHABI', 'JAIPUR', 7), ('ABUDHABI', 'JAIPUR', 8), ('ABUDHABI', 'KOCHI', 0), ('ABUDHABI', 'KOCHI', 1), ('ABUDHABI', 'KOCHI', 2), ('ABUDHABI', 'KOCHI', 3), ('ABUDHABI', 'KOCHI', 4), ('ABUDHABI', 'KOCHI', 5), ('ABUDHABI', 'KOCHI', 6), ('ABUDHABI', 'KOCHI', 7), ('ABUDHABI', 'KOCHI', 8), ('ABUDHABI', 'KOLKATA', 0), ('ABUDHABI', 'KOLKATA', 1), ('ABUDHABI', 'KOLKATA', 2), ('ABUDHABI', 'KOLKATA', 3), ('ABUDHABI', 'KOLKATA', 4), ('ABUDHABI', 'KOLKATA', 5), ('ABUDHABI', 'KOLKATA', 6), ('ABUDHABI', 'KOLKATA', 7), ('ABUDHABI', 'KOZHIKODE', 0), ('ABUDHABI', 'KOZHIKODE', 1), ('ABUDHABI', 'KOZHIKODE', 2), ('ABUDHABI', 'KOZHIKODE', 3), ('ABUDHABI', 'KOZHIKODE', 4), ('ABUDHABI', 'KOZHIKODE', 5), ('ABUDHABI', 'KOZHIKODE', 6), ('ABUDHABI', 'KOZHIKODE', 7), ('ABUDHABI', 'KOZHIKODE', 8), ('ABUDHABI', 'LUCKNOW', 0), ('ABUDHABI', 'LUCKNOW', 1), ('ABUDHABI', 'LUCKNOW', 2), ('ABUDHABI', 'LUCKNOW', 3), ('ABUDHABI', 'LUCKNOW', 4), ('ABUDHABI', 'LUCKNOW', 5), ('ABUDHABI', 'LUCKNOW', 6), ('ABUDHABI', 'LUCKNOW', 7), ('ABUDHABI', 'LUCKNOW', 8), ('ABUDHABI', 'MANGALORE', 0), ('ABUDHABI', 'MANGALORE', 1), ('ABUDHABI', 'MANGALORE', 2), ('ABUDHABI', 'MANGALORE', 3), ('ABUDHABI', 'MANGALORE', 4), ('ABUDHABI', 'MANGALORE', 5), ('ABUDHABI', 'MANGALORE', 6), ('ABUDHABI', 'MANGALORE', 7), ('ABUDHABI', 'MANGALORE', 8), ('ABUDHABI', 'MUMBAI', 0), ('ABUDHABI', 'MUMBAI', 1), ('ABUDHABI', 'MUMBAI', 2), ('ABUDHABI', 'MUMBAI', 3), ('ABUDHABI', 'MUMBAI', 4), ('ABUDHABI', 'MUMBAI', 5), ('ABUDHABI', 'MUMBAI', 6), ('ABUDHABI', 'MUMBAI', 7), ('ABUDHABI', 'MUMBAI', 8), ('ABUDHABI', 'PUNE', 0), ('ABUDHABI', 'PUNE', 1), ('ABUDHABI', 'PUNE', 2), ('ABUDHABI', 'PUNE', 3), ('ABUDHABI', 'PUNE', 4), ('ABUDHABI', 'PUNE', 5), ('ABUDHABI', 'PUNE', 6), ('ABUDHABI', 'PUNE', 7), ('ABUDHABI', 'PUNE', 8), ('ABUDHABI', 'TRIVANDRUM', 0), ('ABUDHABI', 'TRIVANDRUM', 1), ('ABUDHABI', 'TRIVANDRUM', 2), ('ABUDHABI', 'TRIVANDRUM', 3), ('ABUDHABI', 'TRIVANDRUM', 4), ('ABUDHABI', 'TRIVANDRUM', 5), ('ABUDHABI', 'TRIVANDRUM', 6), ('ABUDHABI', 'TRIVANDRUM', 7), ('ABUDHABI', 'TRIVANDRUM', 8), ('AHMEDABAD', 'ABUDHABI', 0), ('AHMEDABAD', 'ABUDHABI', 1), ('AHMEDABAD', 'ABUDHABI', 2), ('AHMEDABAD', 'ABUDHABI', 3), ('AHMEDABAD', 'ABUDHABI', 4), ('AHMEDABAD', 'ABUDHABI', 5), ('AHMEDABAD', 'ABUDHABI', 6), ('AHMEDABAD', 'ABUDHABI', 7), ('AHMEDABAD', 'ABUDHABI', 8), ('AHMEDABAD', 'BANGKOK', 0), ('AHMEDABAD', 'BANGKOK', 1), ('AHMEDABAD', 'BANGKOK', 2), ('AHMEDABAD', 'BANGKOK', 3), ('AHMEDABAD', 'BANGKOK', 4), ('AHMEDABAD', 'BANGKOK', 5), ('AHMEDABAD', 'BANGKOK', 6), ('AHMEDABAD', 'BANGKOK', 7), ('AHMEDABAD', 'BANGKOK', 8), ('AHMEDABAD', 'DOHA', 0), ('AHMEDABAD', 'DOHA', 1), ('AHMEDABAD', 'DOHA', 2), ('AHMEDABAD', 'DOHA', 3), ('AHMEDABAD', 'DOHA', 4), ('AHMEDABAD', 'DOHA', 5), ('AHMEDABAD', 'DOHA', 6), ('AHMEDABAD', 'DOHA', 7), ('AHMEDABAD', 'DOHA', 8), ('AHMEDABAD', 'DUBAI', 0), ('AHMEDABAD', 'DUBAI', 1), ('AHMEDABAD', 'DUBAI', 2), ('AHMEDABAD', 'DUBAI', 3), ('AHMEDABAD', 'DUBAI', 4), ('AHMEDABAD', 'DUBAI', 5), ('AHMEDABAD', 'DUBAI', 6), ('AHMEDABAD', 'DUBAI', 7), ('AHMEDABAD', 'DUBAI', 8), ('AHMEDABAD', 'KUWAIT', 0), ('AHMEDABAD', 'KUWAIT', 1), ('AHMEDABAD', 'KUWAIT', 2), ('AHMEDABAD', 'KUWAIT', 3), ('AHMEDABAD', 'KUWAIT', 4), ('AHMEDABAD', 'KUWAIT', 5), ('AHMEDABAD', 'KUWAIT', 6), 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('SINGAPORE', 'KOCHI', 2), ('SINGAPORE', 'KOCHI', 3), ('SINGAPORE', 'KOCHI', 4), ('SINGAPORE', 'KOCHI', 5), ('SINGAPORE', 'KOCHI', 6), ('SINGAPORE', 'KOCHI', 7), ('SINGAPORE', 'KOCHI', 8), ('SINGAPORE', 'KOLKATA', 0), ('SINGAPORE', 'KOLKATA', 1), ('SINGAPORE', 'KOLKATA', 2), ('SINGAPORE', 'KOLKATA', 3), ('SINGAPORE', 'KOLKATA', 4), ('SINGAPORE', 'KOLKATA', 5), ('SINGAPORE', 'KOLKATA', 6), ('SINGAPORE', 'KOLKATA', 7), ('SINGAPORE', 'KOLKATA', 8), ('SINGAPORE', 'MUMBAI', 0), ('SINGAPORE', 'MUMBAI', 1), ('SINGAPORE', 'MUMBAI', 2), ('SINGAPORE', 'MUMBAI', 3), ('SINGAPORE', 'MUMBAI', 4), ('SINGAPORE', 'MUMBAI', 5), ('SINGAPORE', 'MUMBAI', 6), ('SINGAPORE', 'MUMBAI', 7), ('SINGAPORE', 'MUMBAI', 8), ('SINGAPORE', 'TIRUCHIRAPALLY', 0), ('SINGAPORE', 'TIRUCHIRAPALLY', 1), ('SINGAPORE', 'TIRUCHIRAPALLY', 2), ('SINGAPORE', 'TIRUCHIRAPALLY', 3), ('SINGAPORE', 'TIRUCHIRAPALLY', 4), ('SINGAPORE', 'TIRUCHIRAPALLY', 5), ('SINGAPORE', 'TIRUCHIRAPALLY', 6), ('SINGAPORE', 'TIRUCHIRAPALLY', 7), ('SINGAPORE', 'TIRUCHIRAPALLY', 8), ('SINGAPORE', 'TRIVANDRUM', 0), ('SINGAPORE', 'TRIVANDRUM', 1), ('SINGAPORE', 'TRIVANDRUM', 2), ('SINGAPORE', 'TRIVANDRUM', 3), ('SINGAPORE', 'TRIVANDRUM', 4), ('SINGAPORE', 'TRIVANDRUM', 5), ('SINGAPORE', 'TRIVANDRUM', 6), ('SINGAPORE', 'TRIVANDRUM', 7), ('SINGAPORE', 'TRIVANDRUM', 8), ('SINGAPORE', 'VISAKHAPATNAM', 0), ('SINGAPORE', 'VISAKHAPATNAM', 1), ('SINGAPORE', 'VISAKHAPATNAM', 2), ('SINGAPORE', 'VISAKHAPATNAM', 3), ('SINGAPORE', 'VISAKHAPATNAM', 4), ('SINGAPORE', 'VISAKHAPATNAM', 5), ('SINGAPORE', 'VISAKHAPATNAM', 6), ('SINGAPORE', 'VISAKHAPATNAM', 7), ('SINGAPORE', 'VISAKHAPATNAM', 8), ('SINGAPORE', 'LUCKNOW', 0), ('SINGAPORE', 'LUCKNOW', 1), ('SINGAPORE', 'LUCKNOW', 2), ('SINGAPORE', 'LUCKNOW', 3), ('SINGAPORE', 'LUCKNOW', 4), ('SINGAPORE', 'LUCKNOW', 5), ('SINGAPORE', 'AMRITSAR', 0), ('SINGAPORE', 'AMRITSAR', 1), ('SINGAPORE', 'AMRITSAR', 2), ('SINGAPORE', 'AMRITSAR', 3), ('SYDNEY', 'DELHI', 0), ('SYDNEY', 'DELHI', 1), ('SYDNEY', 'DELHI', 2), ('SYDNEY', 'DELHI', 3), ('SYDNEY', 'DELHI', 4), ('SYDNEY', 'DELHI', 5), ('SYDNEY', 'DELHI', 6), ('SYDNEY', 'DELHI', 7), ('SYDNEY', 'DELHI', 8), ('TAIPAE', 'DELHI', 0), ('TAIPAE', 'DELHI', 1), ('TAIPAE', 'DELHI', 2), ('TAIPAE', 'DELHI', 3), ('TAIPAE', 'DELHI', 4), ('TAIPAE', 'DELHI', 5), ('TAIPAE', 'DELHI', 6), ('TAIPAE', 'DELHI', 7), ('TAIPAE', 'DELHI', 8), ('TAIPAE', 'MUMBAI', 0), ('TAIPAE', 'MUMBAI', 1), ('TAIPAE', 'MUMBAI', 2), ('TAIPAE', 'MUMBAI', 3), ('TAIPAE', 'MUMBAI', 4), ('TAIPAE', 'MUMBAI', 5), ('TAIPAE', 'MUMBAI', 6), ('TAIPAE', 'MUMBAI', 7), ('TAIPAE', 'MUMBAI', 8), ('TASHKENT', 'AMRITSAR', 0), ('TASHKENT', 'AMRITSAR', 1), ('TASHKENT', 'AMRITSAR', 2), ('TASHKENT', 'AMRITSAR', 3), ('TASHKENT', 'AMRITSAR', 4), ('TASHKENT', 'AMRITSAR', 5), ('TASHKENT', 'AMRITSAR', 6), ('TASHKENT', 'AMRITSAR', 7), ('TASHKENT', 'AMRITSAR', 8), ('TASHKENT', 'DELHI', 0), ('TASHKENT', 'DELHI', 1), ('TASHKENT', 'DELHI', 2), ('TASHKENT', 'DELHI', 3), ('TASHKENT', 'DELHI', 4), ('TASHKENT', 'DELHI', 5), ('TASHKENT', 'DELHI', 6), ('TASHKENT', 'DELHI', 7), ('TASHKENT', 'DELHI', 8), ('TEHRAN', 'DELHI', 0), ('TEHRAN', 'DELHI', 1), ('TEHRAN', 'DELHI', 2), ('TEHRAN', 'DELHI', 3), ('TEHRAN', 'DELHI', 4), ('TEHRAN', 'DELHI', 5), ('TEHRAN', 'DELHI', 6), ('TEHRAN', 'DELHI', 7), ('TEHRAN', 'DELHI', 8), ('TEHRAN', 'MUMBAI', 0), ('TEHRAN', 'MUMBAI', 1), ('TEHRAN', 'MUMBAI', 2), ('TEHRAN', 'MUMBAI', 3), ('TEHRAN', 'MUMBAI', 4), ('TEHRAN', 'MUMBAI', 5), ('TEHRAN', 'MUMBAI', 6), ('TEHRAN', 'MUMBAI', 7), ('TEHRAN', 'MUMBAI', 8), ('TELAVIV', 'MUMBAI', 0), ('TELAVIV', 'MUMBAI', 1), ('TELAVIV', 'MUMBAI', 2), ('TELAVIV', 'MUMBAI', 3), ('TELAVIV', 'MUMBAI', 4), ('TELAVIV', 'MUMBAI', 5), ('TELAVIV', 'MUMBAI', 6), ('TELAVIV', 'MUMBAI', 7), ('TELAVIV', 'MUMBAI', 8), ('TOKYO', 'DELHI', 0), ('TOKYO', 'DELHI', 1), ('TOKYO', 'DELHI', 2), ('TOKYO', 'DELHI', 3), ('TOKYO', 'DELHI', 4), ('TOKYO', 'DELHI', 5), ('TOKYO', 'DELHI', 6), ('TOKYO', 'DELHI', 7), ('TOKYO', 'DELHI', 8), ('TORONTO', 'DELHI', 0), ('TORONTO', 'DELHI', 1), ('TORONTO', 'DELHI', 2), ('TORONTO', 'DELHI', 3), ('TORONTO', 'DELHI', 4), ('TORONTO', 'DELHI', 5), ('TORONTO', 'DELHI', 6), ('TORONTO', 'DELHI', 7), ('TORONTO', 'DELHI', 8), ('VIENNA', 'DELHI', 0), ('VIENNA', 'DELHI', 1), ('VIENNA', 'DELHI', 2), ('VIENNA', 'DELHI', 3), ('VIENNA', 'DELHI', 4), ('VIENNA', 'DELHI', 5), ('VIENNA', 'DELHI', 6), ('VIENNA', 'DELHI', 7), ('VIENNA', 'DELHI', 8), ('ZURICH', 'DELHI', 0), ('ZURICH', 'DELHI', 1), ('ZURICH', 'DELHI', 2), ('ZURICH', 'DELHI', 3), ('ZURICH', 'DELHI', 4), ('ZURICH', 'DELHI', 5), ('ZURICH', 'DELHI', 6), ('ZURICH', 'DELHI', 7), ('ZURICH', 'DELHI', 8), ('ZURICH', 'MUMBAI', 0), ('ZURICH', 'MUMBAI', 1), ('ZURICH', 'MUMBAI', 2), ('ZURICH', 'MUMBAI', 3), ('ZURICH', 'MUMBAI', 4), ('ZURICH', 'MUMBAI', 5), ('ZURICH', 'MUMBAI', 6), ('ZURICH', 'MUMBAI', 7), ('ZURICH', 'MUMBAI', 8), ('BAGHDAD', 'DELHI', 0), ('BAGHDAD', 'DELHI', 1), ('BAGHDAD', 'DELHI', 2), ('BAGHDAD', 'DELHI', 3), ('BAGHDAD', 'DELHI', 4), ('BAGHDAD', 'DELHI', 5), ('BAGHDAD', 'DELHI', 6), ('BAGHDAD', 'DELHI', 7), ('REUNION', 'CHENNAI', 0), ('REUNION', 'CHENNAI', 1), ('REUNION', 'CHENNAI', 2), ('REUNION', 'CHENNAI', 3), ('REUNION', 'CHENNAI', 4), ('REUNION', 'CHENNAI', 5), ('REUNION', 'CHENNAI', 6), ('REUNION', 'CHENNAI', 7), ('DON MUEANG', 'BENGALURU', 0), ('DON MUEANG', 'BENGALURU', 1), ('DON MUEANG', 'BENGALURU', 2), ('DON MUEANG', 'BENGALURU', 3), ('DON MUEANG', 'BENGALURU', 4), ('DON MUEANG', 'BENGALURU', 5), ('DON MUEANG', 'BENGALURU', 6), ('DON MUEANG', 'CHENNAI', 0), ('DON MUEANG', 'CHENNAI', 1), ('DON MUEANG', 'CHENNAI', 2), ('DON MUEANG', 'CHENNAI', 3), ('DON MUEANG', 'CHENNAI', 4), ('DON MUEANG', 'CHENNAI', 5), ('DON MUEANG', 'CHENNAI', 6), ('DON MUEANG', 'GAYA', 0), ('DON MUEANG', 'KOCHI', 0), ('DON MUEANG', 'KOCHI', 1), ('DON MUEANG', 'KOCHI', 2), ('DON MUEANG', 'KOCHI', 3), ('DON MUEANG', 'KOLKATA', 0), ('DON MUEANG', 'KOLKATA', 1), ('KANDAHAR', 'DELHI', 0), ('KANDAHAR', 'DELHI', 1), ('KANDAHAR', 'DELHI', 2), ('KANDAHAR', 'DELHI', 3), ('KANDAHAR', 'DELHI', 4), ('KANDAHAR', 'DELHI', 5), ('MANCHESTER', 'GOA', 0), ('MANCHESTER', 'GOA', 1), ('MANCHESTER', 'GOA', 2), ('MANCHESTER', 'GOA', 3), ('MANCHESTER', 'GOA', 4), ('MANCHESTER', 'GOA', 5), ('SAN FRANCISCO', 'BENGALURU', 0), ('SAN FRANCISCO', 'BENGALURU', 1), ('SAN FRANCISCO', 'BENGALURU', 2), ('SAN FRANCISCO', 'BENGALURU', 3), ('SAN FRANCISCO', 'BENGALURU', 4), ('SAN FRANCISCO', 'BENGALURU', 5), ('SAN FRANCISCO', 'DELHI', 0), ('SAN FRANCISCO', 'DELHI', 1), ('SAN FRANCISCO', 'DELHI', 2), ('SAN FRANCISCO', 'DELHI', 3), ('SAN FRANCISCO', 'DELHI', 4), ('SAN FRANCISCO', 'DELHI', 5), ('BASRA', 'DELHI', 0), ('BASRA', 'DELHI', 1), ('BASRA', 'DELHI', 2), ('BASRA', 'DELHI', 3), ('BASRA', 'DELHI', 4), ('RASALKHAMA', 'KOZHIKODE', 0), ('RASALKHAMA', 'KOZHIKODE', 1), ('RASALKHAMA', 'KOZHIKODE', 2), ('RASALKHAMA', 'KOZHIKODE', 3), ('RASALKHAMA', 'KOZHIKODE', 4), ('RASALKHAMA', 'TRIVANDRUM', 0), ('ANTALYA', 'DELHI', 0), ('ANTALYA', 'DELHI', 1), ('ANTALYA', 'DELHI', 2), ('ANTALYA', 'DELHI', 3), ('ANTALYA', 'MUMBAI', 0), ('ANTALYA', 'MUMBAI', 1), ('ANTALYA', 'MUMBAI', 2), ('ANTALYA', 'MUMBAI', 3), ('BAKU', 'GOA', 0), ('CHANDIGARH', 'DUBAI', 0), ('CHANDIGARH', 'DUBAI', 1), ('CHANDIGARH', 'DUBAI', 2), ('CHANDIGARH', 'SHARJAH', 0), ('CHANDIGARH', 'SHARJAH', 1), ('CHANDIGARH', 'SHARJAH', 2), ('MADRID', 'DELHI', 0), ('MADRID', 'DELHI', 1), ('MASHHAD', 'DELHI', 0), ('MASHHAD', 'DELHI', 1), ('TANGERANG', 'MUMBAI', 0), ('VANCOUVER', 'DELHI', 0), ('VANCOUVER', 'DELHI', 1)])

MDG.adj

MultiAdjacencyView({'ABUDHABI': {'AHMEDABAD': {0: {'weight': 0.041930980108736714}, 1: {'weight': 0.0659318999359406}, 2: {'weight': 0.05957195184047568}, 3: {'weight': 0.07623067952234687}, 4: {'weight': 0.08492633169625992}, 5: {'weight': 0.08351702501601485}, 6: {'weight': 0.09112531003104417}, 7: {'weight': 0.06027496263201984}, 8: {'weight': 0.061733545769616135}}, 'BENGALURU': {0: {'weight': 0.0898178413626583}, 1: {'weight': 0.10158834447528786}, 2: {'weight': 0.10943972667991655}, 3: {'weight': 0.12064847817874214}, 4: {'weight': 0.11583909594126246}, 5: {'weight': 0.10188071812223846}, 6: {'weight': 0.12281335720503934}, 7: {'weight': 0.10673937681706937}, 8: {'weight': 0.11949869417388019}}, 'CHENNAI': {0: {'weight': 0.10432154530970253}, 1: {'weight': 0.0980897160033508}, 2: {'weight': 0.12416024703930618}, 3: {'weight': 0.13469226852384159}, 4: {'weight': 0.1409076723444096}, 5: {'weight': 0.12312544143493044}, 6: {'weight': 0.15286871109213054}, 7: {'weight': 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MDG.degree

DiMultiDegreeView({'ABUDHABI': 268, 'AHMEDABAD': 180, 'BENGALURU': 414, 'CHENNAI': 464, 'DELHI': 1232, 'GOA': 178, 'HYDERABAD': 322, 'JAIPUR': 78, 'KOCHI': 296, 'KOLKATA': 274, 'KOZHIKODE': 226, 'LUCKNOW': 112, 'MANGALORE': 132, 'MUMBAI': 892, 'PUNE': 72, 'TRIVANDRUM': 270, 'ADDIS ABABA': 52, 'ADEN': 18, 'AL AIN': 20, 'ALMATY': 30, 'AMSTERDAM': 32, 'ASHGABAT': 36, 'AMRITSAR': 144, 'BAHRAIN': 162, 'BANGKOK': 170, 'GAYA': 74, 'VARANASI': 72, 'BIRMINGHAM': 36, 'BISHKEK': 18, 'BRUSELLS': 36, 'CAIRO': 18, 'CHICAGO': 36, 'CHITTAGONG': 18, 'COLOGNE': 30, 'COLOMBO': 194, 'MADURAI': 36, 'TIRUCHIRAPALLY': 78, 'DAMMAM': 162, 'DHAKA': 72, 'DOHA': 244, 'DUBAI': 348, 'VISAKHAPATNAM': 60, 'DUSHANBE': 18, 'FRANKFURT': 136, 'GUANGZHOU': 36, 'HAA DHAALU ATOLL': 18, 'HELNSKI': 18, 'HO CHI MINH CITY': 32, 'HONG KONG': 104, 'ISTANBUL': 36, 'JEDDAH': 144, 'JOHANNESBERG': 2, 'KABUL': 18, 'KARACHI': 36, 'KATHMANDU': 86, 'KUALALUMPUR': 202, 'KUNMING': 30, 'KUWAIT': 194, 'LAHORE': 18, 'LEIPZIG': 44, 'LONDON': 138, 'LUXEMBOURG': 44, 'MALE': 82, 'MALPENSA': 28, 'MANDALAY': 18, 'MARUITIUS': 72, 'MEDINA': 66, 'MELBOURNE': 18, 'MOSCOW': 40, 'MUENCHEN': 36, 'MUSCAT': 234, 'NAIROBI': 18, 'NARITA': 36, 'NAVOI': 34, 'NEW YORK': 36, 'NEWARK': 54, 'OSAKA': 46, 'PARIS': 72, 'PARO': 36, 'PEKING': 18, 'RANGOON': 58, 'RIYADH': 162, 'ROME': 18, 'SALALAH': 54, 'SANAA': 18, 'SEOUL': 54, 'SEYCHELLES': 18, 'SHANGHAI': 36, 'SHARJAH': 302, 'COIMBATORE': 36, 'NAGPUR': 28, 'SHENZHEN': 18, 'SINGAPORE': 236, 'SYDNEY': 18, 'TAIPAE': 36, 'TASHKENT': 36, 'TEHRAN': 36, 'TELAVIV': 18, 'TOKYO': 18, 'TORONTO': 18, 'VIENNA': 18, 'ZURICH': 36, 'BAGHDAD': 16, 'REUNION': 16, 'DON MUEANG': 42, 'KANDAHAR': 12, 'MANCHESTER': 12, 'SAN FRANCISCO': 24, 'BASRA': 10, 'RASALKHAMA': 12, 'ANTALYA': 16, 'BAKU': 2, 'CHANDIGARH': 12, 'MADRID': 4, 'MASHHAD': 4, 'TANGERANG': 2, 'VANCOUVER': 4})

MDG.in_degree

InMultiDegreeView({'ABUDHABI': 134, 'AHMEDABAD': 90, 'BENGALURU': 207, 'CHENNAI': 232, 'DELHI': 616, 'GOA': 89, 'HYDERABAD': 161, 'JAIPUR': 39, 'KOCHI': 148, 'KOLKATA': 137, 'KOZHIKODE': 113, 'LUCKNOW': 56, 'MANGALORE': 66, 'MUMBAI': 446, 'PUNE': 36, 'TRIVANDRUM': 135, 'ADDIS ABABA': 26, 'ADEN': 9, 'AL AIN': 10, 'ALMATY': 15, 'AMSTERDAM': 16, 'ASHGABAT': 18, 'AMRITSAR': 72, 'BAHRAIN': 81, 'BANGKOK': 85, 'GAYA': 37, 'VARANASI': 36, 'BIRMINGHAM': 18, 'BISHKEK': 9, 'BRUSELLS': 18, 'CAIRO': 9, 'CHICAGO': 18, 'CHITTAGONG': 9, 'COLOGNE': 15, 'COLOMBO': 97, 'MADURAI': 18, 'TIRUCHIRAPALLY': 39, 'DAMMAM': 81, 'DHAKA': 36, 'DOHA': 122, 'DUBAI': 174, 'VISAKHAPATNAM': 30, 'DUSHANBE': 9, 'FRANKFURT': 68, 'GUANGZHOU': 18, 'HAA DHAALU ATOLL': 9, 'HELNSKI': 9, 'HO CHI MINH CITY': 16, 'HONG KONG': 52, 'ISTANBUL': 18, 'JEDDAH': 72, 'JOHANNESBERG': 1, 'KABUL': 9, 'KARACHI': 18, 'KATHMANDU': 43, 'KUALALUMPUR': 101, 'KUNMING': 15, 'KUWAIT': 97, 'LAHORE': 9, 'LEIPZIG': 22, 'LONDON': 69, 'LUXEMBOURG': 22, 'MALE': 41, 'MALPENSA': 14, 'MANDALAY': 9, 'MARUITIUS': 36, 'MEDINA': 33, 'MELBOURNE': 9, 'MOSCOW': 20, 'MUENCHEN': 18, 'MUSCAT': 117, 'NAIROBI': 9, 'NARITA': 18, 'NAVOI': 17, 'NEW YORK': 18, 'NEWARK': 27, 'OSAKA': 23, 'PARIS': 36, 'PARO': 18, 'PEKING': 9, 'RANGOON': 29, 'RIYADH': 81, 'ROME': 9, 'SALALAH': 27, 'SANAA': 9, 'SEOUL': 27, 'SEYCHELLES': 9, 'SHANGHAI': 18, 'SHARJAH': 151, 'COIMBATORE': 18, 'NAGPUR': 14, 'SHENZHEN': 9, 'SINGAPORE': 118, 'SYDNEY': 9, 'TAIPAE': 18, 'TASHKENT': 18, 'TEHRAN': 18, 'TELAVIV': 9, 'TOKYO': 9, 'TORONTO': 9, 'VIENNA': 9, 'ZURICH': 18, 'BAGHDAD': 8, 'REUNION': 8, 'DON MUEANG': 21, 'KANDAHAR': 6, 'MANCHESTER': 6, 'SAN FRANCISCO': 12, 'BASRA': 5, 'RASALKHAMA': 6, 'ANTALYA': 8, 'BAKU': 1, 'CHANDIGARH': 6, 'MADRID': 2, 'MASHHAD': 2, 'TANGERANG': 1, 'VANCOUVER': 2})

MDG.out_degree

OutMultiDegreeView({'ABUDHABI': 134, 'AHMEDABAD': 90, 'BENGALURU': 207, 'CHENNAI': 232, 'DELHI': 616, 'GOA': 89, 'HYDERABAD': 161, 'JAIPUR': 39, 'KOCHI': 148, 'KOLKATA': 137, 'KOZHIKODE': 113, 'LUCKNOW': 56, 'MANGALORE': 66, 'MUMBAI': 446, 'PUNE': 36, 'TRIVANDRUM': 135, 'ADDIS ABABA': 26, 'ADEN': 9, 'AL AIN': 10, 'ALMATY': 15, 'AMSTERDAM': 16, 'ASHGABAT': 18, 'AMRITSAR': 72, 'BAHRAIN': 81, 'BANGKOK': 85, 'GAYA': 37, 'VARANASI': 36, 'BIRMINGHAM': 18, 'BISHKEK': 9, 'BRUSELLS': 18, 'CAIRO': 9, 'CHICAGO': 18, 'CHITTAGONG': 9, 'COLOGNE': 15, 'COLOMBO': 97, 'MADURAI': 18, 'TIRUCHIRAPALLY': 39, 'DAMMAM': 81, 'DHAKA': 36, 'DOHA': 122, 'DUBAI': 174, 'VISAKHAPATNAM': 30, 'DUSHANBE': 9, 'FRANKFURT': 68, 'GUANGZHOU': 18, 'HAA DHAALU ATOLL': 9, 'HELNSKI': 9, 'HO CHI MINH CITY': 16, 'HONG KONG': 52, 'ISTANBUL': 18, 'JEDDAH': 72, 'JOHANNESBERG': 1, 'KABUL': 9, 'KARACHI': 18, 'KATHMANDU': 43, 'KUALALUMPUR': 101, 'KUNMING': 15, 'KUWAIT': 97, 'LAHORE': 9, 'LEIPZIG': 22, 'LONDON': 69, 'LUXEMBOURG': 22, 'MALE': 41, 'MALPENSA': 14, 'MANDALAY': 9, 'MARUITIUS': 36, 'MEDINA': 33, 'MELBOURNE': 9, 'MOSCOW': 20, 'MUENCHEN': 18, 'MUSCAT': 117, 'NAIROBI': 9, 'NARITA': 18, 'NAVOI': 17, 'NEW YORK': 18, 'NEWARK': 27, 'OSAKA': 23, 'PARIS': 36, 'PARO': 18, 'PEKING': 9, 'RANGOON': 29, 'RIYADH': 81, 'ROME': 9, 'SALALAH': 27, 'SANAA': 9, 'SEOUL': 27, 'SEYCHELLES': 9, 'SHANGHAI': 18, 'SHARJAH': 151, 'COIMBATORE': 18, 'NAGPUR': 14, 'SHENZHEN': 9, 'SINGAPORE': 118, 'SYDNEY': 9, 'TAIPAE': 18, 'TASHKENT': 18, 'TEHRAN': 18, 'TELAVIV': 9, 'TOKYO': 9, 'TORONTO': 9, 'VIENNA': 9, 'ZURICH': 18, 'BAGHDAD': 8, 'REUNION': 8, 'DON MUEANG': 21, 'KANDAHAR': 6, 'MANCHESTER': 6, 'SAN FRANCISCO': 12, 'BASRA': 5, 'RASALKHAMA': 6, 'ANTALYA': 8, 'BAKU': 1, 'CHANDIGARH': 6, 'MADRID': 2, 'MASHHAD': 2, 'TANGERANG': 1, 'VANCOUVER': 2})

pos = nx.spring_layout(MDG)
fig = plt.figure(figsize=(40, 40)) 
nx.draw(MDG, pos, with_labels=True, connectionstyle='arc3, rad = 0.1')
edge_labels=dict([((u,v,),d['weight'])
             for u,v,d in MDG.edges(data=True)])
plt.axis('equal') 
plt.show() 
fig.savefig('cityTrafficNetwork.svg') 

nx.eigenvector_centrality(DG12)

{'ABUDHABI': 9.776204996427335e-05,
 'AHMEDABAD': 0.09199408901638104,
 'BENGALURU': 0.22064894676936442,
 'CHENNAI': 0.2482178448592903,
 'DELHI': 0.6801305816014552,
 'GOA': 0.11037335440966438,
 'HYDERABAD': 0.17470078328615604,
 'JAIPUR': 0.055235558229814435,
 'KOCHI': 0.15632151789287269,
 'KOLKATA': 0.15632151789287269,
 'KOZHIKODE': 0.11956298710630611,
 'LUCKNOW': 0.07361482362309775,
 'MANGALORE': 0.07361482362309775,
 'MUMBAI': 0.5055275603652616,
 'PUNE': 0.03685629283653105,
 'TRIVANDRUM': 0.15632151789287269,
 'ADDIS ABABA': 9.776204996427335e-05,
 'ADEN': 9.776204996427335e-05,
 'AL AIN': 9.776204996427335e-05,
 'ALMATY': 9.776204996427335e-05,
 'AMSTERDAM': 9.776204996427335e-05,
 'ASHGABAT': 9.776204996427335e-05,
 'AMRITSAR': 0.09199408901638104,
 'BAHRAIN': 9.776204996427335e-05,
 'BANGKOK': 9.776204996427335e-05,
 'GAYA': 0.04604592553317275,
 'VARANASI': 0.04604592553317275,
 'BIRMINGHAM': 9.776204996427335e-05,
 'BISHKEK': 9.776204996427335e-05,
 'BRUSELLS': 9.776204996427335e-05,
 'CAIRO': 9.776204996427335e-05,
 'CHICAGO': 9.776204996427335e-05,
 'CHITTAGONG': 9.776204996427335e-05,
 'COLOGNE': 9.776204996427335e-05,
 'COLOMBO': 9.776204996427335e-05,
 'MADURAI': 0.018477027443247675,
 'TIRUCHIRAPALLY': 0.04604592553317275,
 'DAMMAM': 9.776204996427335e-05,
 'DHAKA': 9.776204996427335e-05,
 'DOHA': 9.776204996427335e-05,
 'DUBAI': 9.776204996427335e-05,
 'VISAKHAPATNAM': 0.03685629283653105,
 'DUSHANBE': 9.776204996427335e-05,
 'FRANKFURT': 9.776204996427335e-05,
 'GUANGZHOU': 9.776204996427335e-05,
 'HAA DHAALU ATOLL': 9.776204996427335e-05,
 'HELNSKI': 9.776204996427335e-05,
 'HO CHI MINH CITY': 9.776204996427335e-05,
 'HONG KONG': 9.776204996427335e-05,
 'ISTANBUL': 9.776204996427335e-05,
 'JEDDAH': 9.776204996427335e-05,
 'JOHANNESBERG': 9.776204996427335e-05,
 'KABUL': 9.776204996427335e-05,
 'KARACHI': 9.776204996427335e-05,
 'KATHMANDU': 9.776204996427335e-05,
 'KUALALUMPUR': 9.776204996427335e-05,
 'KUNMING': 9.776204996427335e-05,
 'KUWAIT': 9.776204996427335e-05,
 'LAHORE': 9.776204996427335e-05,
 'LEIPZIG': 9.776204996427335e-05,
 'LONDON': 9.776204996427335e-05,
 'LUXEMBOURG': 9.776204996427335e-05,
 'MALE': 9.776204996427335e-05,
 'MALPENSA': 9.776204996427335e-05,
 'MANDALAY': 9.776204996427335e-05,
 'MARUITIUS': 9.776204996427335e-05,
 'MEDINA': 9.776204996427335e-05,
 'MELBOURNE': 9.776204996427335e-05,
 'MOSCOW': 9.776204996427335e-05,
 'MUENCHEN': 9.776204996427335e-05,
 'MUSCAT': 9.776204996427335e-05,
 'NAIROBI': 9.776204996427335e-05,
 'NARITA': 9.776204996427335e-05,
 'NAVOI': 9.776204996427335e-05,
 'NEW YORK': 9.776204996427335e-05,
 'NEWARK': 9.776204996427335e-05,
 'OSAKA': 9.776204996427335e-05,
 'PARIS': 9.776204996427335e-05,
 'PARO': 9.776204996427335e-05,
 'PEKING': 9.776204996427335e-05,
 'RANGOON': 9.776204996427335e-05,
 'RIYADH': 9.776204996427335e-05,
 'ROME': 9.776204996427335e-05,
 'SALALAH': 9.776204996427335e-05,
 'SANAA': 9.776204996427335e-05,
 'SEOUL': 9.776204996427335e-05,
 'SEYCHELLES': 9.776204996427335e-05,
 'SHANGHAI': 9.776204996427335e-05,
 'SHARJAH': 9.776204996427335e-05,
 'COIMBATORE': 0.018477027443247675,
 'NAGPUR': 0.018477027443247675,
 'SHENZHEN': 9.776204996427335e-05,
 'SINGAPORE': 9.776204996427335e-05,
 'SYDNEY': 9.776204996427335e-05,
 'TAIPAE': 9.776204996427335e-05,
 'TASHKENT': 9.776204996427335e-05,
 'TEHRAN': 9.776204996427335e-05,
 'TELAVIV': 9.776204996427335e-05,
 'TOKYO': 9.776204996427335e-05,
 'TORONTO': 9.776204996427335e-05,
 'VIENNA': 9.776204996427335e-05,
 'ZURICH': 9.776204996427335e-05,
 'BAGHDAD': 9.776204996427335e-05,
 'REUNION': 9.776204996427335e-05,
 'DON MUEANG': 9.776204996427335e-05,
 'KANDAHAR': 9.776204996427335e-05,
 'MANCHESTER': 9.776204996427335e-05,
 'SAN FRANCISCO': 9.776204996427335e-05,
 'BASRA': 9.776204996427335e-05,
 'RASALKHAMA': 9.776204996427335e-05,
 'ANTALYA': 9.776204996427335e-05,
 'BAKU': 9.776204996427335e-05,
 'CHANDIGARH': 0.018477027443247675,
 'MADRID': 9.776204996427335e-05,
 'MASHHAD': 9.776204996427335e-05,
 'TANGERANG': 9.776204996427335e-05,
 'VANCOUVER': 9.776204996427335e-05}

nx.eigenvector_centrality(DG21, max_iter=1000)

{'AHMEDABAD': 0.00015322317099288678,
 'ABUDHABI': 0.27365658339329596,
 'BENGALURU': 0.00015322317099288678,
 'CHENNAI': 0.00015322317099288678,
 'DELHI': 0.00015322317099288678,
 'GOA': 0.00015322317099288678,
 'HYDERABAD': 0.00015322317099288678,
 'JAIPUR': 0.00015322317099288678,
 'KOCHI': 0.00015322317099288678,
 'KOLKATA': 0.00015322317099288678,
 'KOZHIKODE': 0.00015322317099288678,
 'LUCKNOW': 0.00015322317099288678,
 'MANGALORE': 0.00015322317099288678,
 'MUMBAI': 0.00015322317099288678,
 'PUNE': 0.00015322317099288678,
 'TRIVANDRUM': 0.00015322317099288678,
 'ADDIS ABABA': 0.054853895215453544,
 'ADEN': 0.01838678051914644,
 'AL AIN': 0.03662033786729999,
 'ALMATY': 0.03662033786729999,
 'AMSTERDAM': 0.054853895215453544,
 'AMRITSAR': 0.00015322317099288678,
 'ASHGABAT': 0.03662033786729999,
 'BAHRAIN': 0.1642552393043753,
 'BANGKOK': 0.20072235400068245,
 'GAYA': 0.00015322317099288678,
 'VARANASI': 0.00015322317099288678,
 'BIRMINGHAM': 0.03662033786729999,
 'BISHKEK': 0.01838678051914644,
 'BRUSELLS': 0.03662033786729999,
 'CAIRO': 0.01838678051914644,
 'CHICAGO': 0.03662033786729999,
 'CHITTAGONG': 0.01838678051914644,
 'COLOGNE': 0.03662033786729999,
 'COLOMBO': 0.20072235400068245,
 'MADURAI': 0.00015322317099288678,
 'TIRUCHIRAPALLY': 0.00015322317099288678,
 'DAMMAM': 0.1642552393043753,
 'DHAKA': 0.07308745256360677,
 'DOHA': 0.2554230260451424,
 'DUBAI': 0.36482437013406394,
 'VISAKHAPATNAM': 0.00015322317099288678,
 'DUSHANBE': 0.01838678051914644,
 'FRANKFURT': 0.14602168195622164,
 'GUANGZHOU': 0.03662033786729999,
 'HAA DHAALU ATOLL': 0.01838678051914644,
 'HELNSKI': 0.01838678051914644,
 'HO CHI MINH CITY': 0.03662033786729999,
 'HONG KONG': 0.10955456725991378,
 'ISTANBUL': 0.03662033786729999,
 'JEDDAH': 0.14602168195622164,
 'JOHANNESBERG': 0.01838678051914644,
 'KABUL': 0.01838678051914644,
 'KARACHI': 0.03662033786729999,
 'KATHMANDU': 0.0913210099117603,
 'KUALALUMPUR': 0.21895591134883596,
 'KUNMING': 0.03662033786729999,
 'KUWAIT': 0.20072235400068245,
 'LAHORE': 0.01838678051914644,
 'LEIPZIG': 0.054853895215453544,
 'LONDON': 0.14602168195622164,
 'LUXEMBOURG': 0.054853895215453544,
 'MALE': 0.12778812460806813,
 'MALPENSA': 0.03662033786729999,
 'MANDALAY': 0.01838678051914644,
 'MARUITIUS': 0.07308745256360677,
 'MEDINA': 0.07308745256360677,
 'MELBOURNE': 0.01838678051914644,
 'MOSCOW': 0.054853895215453544,
 'MUENCHEN': 0.03662033786729999,
 'MUSCAT': 0.2371894686969896,
 'NAIROBI': 0.01838678051914644,
 'NARITA': 0.03662033786729999,
 'NAVOI': 0.03662033786729999,
 'NEW YORK': 0.03662033786729999,
 'NEWARK': 0.054853895215453544,
 'OSAKA': 0.054853895215453544,
 'PARIS': 0.07308745256360677,
 'PARO': 0.03662033786729999,
 'PEKING': 0.01838678051914644,
 'RANGOON': 0.07308745256360677,
 'RIYADH': 0.1642552393043753,
 'ROME': 0.01838678051914644,
 'SALALAH': 0.054853895215453544,
 'SANAA': 0.01838678051914644,
 'SEOUL': 0.054853895215453544,
 'SEYCHELLES': 0.01838678051914644,
 'SHANGHAI': 0.03662033786729999,
 'SHARJAH': 0.3830579274822174,
 'COIMBATORE': 0.00015322317099288678,
 'NAGPUR': 0.00015322317099288678,
 'SHENZHEN': 0.01838678051914644,
 'SINGAPORE': 0.2554230260451424,
 'SYDNEY': 0.01838678051914644,
 'TAIPAE': 0.03662033786729999,
 'TASHKENT': 0.03662033786729999,
 'TEHRAN': 0.03662033786729999,
 'TELAVIV': 0.01838678051914644,
 'TOKYO': 0.01838678051914644,
 'TORONTO': 0.01838678051914644,
 'VIENNA': 0.01838678051914644,
 'ZURICH': 0.03662033786729999,
 'BAGHDAD': 0.01838678051914644,
 'REUNION': 0.01838678051914644,
 'DON MUEANG': 0.0913210099117603,
 'KANDAHAR': 0.01838678051914644,
 'MANCHESTER': 0.01838678051914644,
 'SAN FRANCISCO': 0.03662033786729999,
 'BASRA': 0.01838678051914644,
 'RASALKHAMA': 0.03662033786729999,
 'ANTALYA': 0.03662033786729999,
 'BAKU': 0.01838678051914644,
 'CHANDIGARH': 0.00015322317099288678,
 'MADRID': 0.01838678051914644,
 'MASHHAD': 0.01838678051914644,
 'TANGERANG': 0.01838678051914644,
 'VANCOUVER': 0.01838678051914644}

nx.closeness_centrality(MDG)

{'ABUDHABI': 0.5087719298245614,
 'AHMEDABAD': 0.380327868852459,
 'BENGALURU': 0.4187725631768953,
 'CHENNAI': 0.4280442804428044,
 'DELHI': 0.655367231638418,
 'GOA': 0.380327868852459,
 'HYDERABAD': 0.40418118466898956,
 'JAIPUR': 0.3706070287539936,
 'KOCHI': 0.39862542955326463,
 'KOLKATA': 0.39862542955326463,
 'KOZHIKODE': 0.38283828382838286,
 'LUCKNOW': 0.37540453074433655,
 'MANGALORE': 0.3706070287539936,
 'MUMBAI': 0.5395348837209303,
 'PUNE': 0.3613707165109034,
 'TRIVANDRUM': 0.39862542955326463,
 'ADDIS ABABA': 0.43609022556390975,
 'ADEN': 0.3515151515151515,
 'AL AIN': 0.2885572139303483,
 'ALMATY': 0.40559440559440557,
 'AMSTERDAM': 0.43609022556390975,
 'ASHGABAT': 0.4,
 'AMRITSAR': 0.37540453074433655,
 'BAHRAIN': 0.4715447154471545,
 'BANGKOK': 0.4715447154471545,
 'GAYA': 0.34421364985163205,
 'VARANASI': 0.3682539682539683,
 'BIRMINGHAM': 0.4,
 'BISHKEK': 0.3972602739726027,
 'BRUSELLS': 0.4264705882352941,
 'CAIRO': 0.3515151515151515,
 'CHICAGO': 0.4,
 'CHITTAGONG': 0.2857142857142857,
 'COLOGNE': 0.4264705882352941,
 'COLOMBO': 0.48739495798319327,
 'MADURAI': 0.3569230769230769,
 'TIRUCHIRAPALLY': 0.3682539682539683,
 'DAMMAM': 0.4715447154471545,
 'DHAKA': 0.44274809160305345,
 'DOHA': 0.5043478260869565,
 'DUBAI': 0.5321100917431193,
 'VISAKHAPATNAM': 0.3613707165109034,
 'DUSHANBE': 0.3972602739726027,
 'FRANKFURT': 0.4566929133858268,
 'GUANGZHOU': 0.4264705882352941,
 'HAA DHAALU ATOLL': 0.2857142857142857,
 'HELNSKI': 0.3972602739726027,
 'HO CHI MINH CITY': 0.4264705882352941,
 'HONG KONG': 0.4496124031007752,
 'ISTANBUL': 0.4264705882352941,
 'JEDDAH': 0.464,
 'JOHANNESBERG': 0.3515151515151515,
 'KABUL': 0.3972602739726027,
 'KARACHI': 0.4264705882352941,
 'KATHMANDU': 0.44274809160305345,
 'KUALALUMPUR': 0.49572649572649574,
 'KUNMING': 0.40559440559440557,
 'KUWAIT': 0.48739495798319327,
 'LAHORE': 0.3972602739726027,
 'LEIPZIG': 0.43283582089552236,
 'LONDON': 0.4603174603174603,
 'LUXEMBOURG': 0.43609022556390975,
 'MALE': 0.464,
 'MALPENSA': 0.4264705882352941,
 'MANDALAY': 0.25663716814159293,
 'MARUITIUS': 0.4393939393939394,
 'MEDINA': 0.4393939393939394,
 'MELBOURNE': 0.3972602739726027,
 'MOSCOW': 0.4084507042253521,
 'MUENCHEN': 0.4264705882352941,
 'MUSCAT': 0.49572649572649574,
 'NAIROBI': 0.3515151515151515,
 'NARITA': 0.4264705882352941,
 'NAVOI': 0.4264705882352941,
 'NEW YORK': 0.4264705882352941,
 'NEWARK': 0.42962962962962964,
 'OSAKA': 0.43609022556390975,
 'PARIS': 0.4393939393939394,
 'PARO': 0.40559440559440557,
 'PEKING': 0.3972602739726027,
 'RANGOON': 0.4142857142857143,
 'RIYADH': 0.4715447154471545,
 'ROME': 0.3972602739726027,
 'SALALAH': 0.29292929292929293,
 'SANAA': 0.3515151515151515,
 'SEOUL': 0.43609022556390975,
 'SEYCHELLES': 0.3515151515151515,
 'SHANGHAI': 0.4264705882352941,
 'SHARJAH': 0.5321100917431193,
 'COIMBATORE': 0.3547400611620795,
 'NAGPUR': 0.3547400611620795,
 'SHENZHEN': 0.3515151515151515,
 'SINGAPORE': 0.49572649572649574,
 'SYDNEY': 0.3972602739726027,
 'TAIPAE': 0.4264705882352941,
 'TASHKENT': 0.4,
 'TEHRAN': 0.4264705882352941,
 'TELAVIV': 0.3515151515151515,
 'TOKYO': 0.3972602739726027,
 'TORONTO': 0.3972602739726027,
 'VIENNA': 0.3972602739726027,
 'ZURICH': 0.4264705882352941,
 'BAGHDAD': 0.3972602739726027,
 'REUNION': 0.3005181347150259,
 'DON MUEANG': 0.3258426966292135,
 'KANDAHAR': 0.3972602739726027,
 'MANCHESTER': 0.2761904761904762,
 'SAN FRANCISCO': 0.4027777777777778,
 'BASRA': 0.3972602739726027,
 'RASALKHAMA': 0.2885572139303483,
 'ANTALYA': 0.4264705882352941,
 'BAKU': 0.2761904761904762,
 'CHANDIGARH': 0.3569230769230769,
 'MADRID': 0.3972602739726027,
 'MASHHAD': 0.3972602739726027,
 'TANGERANG': 0.3515151515151515,
 'VANCOUVER': 0.3972602739726027}

nx.draw(DG21, pos, with_labels=True, connectionstyle='arc3, rad = 0.1')
edge_labels=dict([((u,v,),d['weight'])
             for u,v,d in MDG.edges(data=True)])
plt.axis('equal') 
plt.show() 

nx.betweenness_centrality(G)

{'ABUDHABI': 0.02965400017749279,
 'AHMEDABAD': 0.003502124864752629,
 'BENGALURU': 0.026385506643405644,
 'CHENNAI': 0.04746999572855074,
 'DELHI': 0.5194158721675942,
 'GOA': 0.03924338904979626,
 'HYDERABAD': 0.014911045728131124,
 'JAIPUR': 0.0009761243802485486,
 'KOCHI': 0.016453148245605297,
 'KOLKATA': 0.034327874753704156,
 'KOZHIKODE': 0.020178547802871934,
 'LUCKNOW': 0.0016154765030639228,
 'MANGALORE': 0.0011289698623627412,
 'MUMBAI': 0.27114397312898286,
 'PUNE': 0.0003592057682940828,
 'TRIVANDRUM': 0.04655680479626339,
 'ADDIS ABABA': 0.001632301097863998,
 'ADEN': 0.0,
 'AL AIN': 3.736451353236035e-05,
 'ALMATY': 0.0017720303601110412,
 'AMSTERDAM': 0.001632301097863998,
 'ASHGABAT': 0.0005962909700470743,
 'AMRITSAR': 0.007599477084102243,
 'BAHRAIN': 0.01087217391536128,
 'BANGKOK': 0.02571808307486854,
 'GAYA': 0.018040156337415993,
 'VARANASI': 0.0015090857758221744,
 'BIRMINGHAM': 0.0005962909700470743,
 'BISHKEK': 0.0,
 'BRUSELLS': 0.0008739684171712054,
 'CAIRO': 0.0,
 'CHICAGO': 0.00029301372171902716,
 'CHITTAGONG': 0.0,
 'COLOGNE': 0.0008739684171712054,
 'COLOMBO': 0.034461663824714696,
 'MADURAI': 0.00010145986514161635,
 'TIRUCHIRAPALLY': 0.0006630210402254232,
 'DAMMAM': 0.01087217391536128,
 'DHAKA': 0.003253428429298128,
 'DOHA': 0.03142547713758944,
 'DUBAI': 0.05915660194453823,
 'VISAKHAPATNAM': 0.00025603924672530697,
 'DUSHANBE': 0.0,
 'FRANKFURT': 0.009586545374939763,
 'GUANGZHOU': 0.0008739684171712054,
 'HAA DHAALU ATOLL': 0.0,
 'HELNSKI': 0.0,
 'HO CHI MINH CITY': 0.0008739684171712054,
 'HONG KONG': 0.004652353775092221,
 'ISTANBUL': 0.0008739684171712054,
 'JEDDAH': 0.007813132488232354,
 'JOHANNESBERG': 0.0,
 'KABUL': 0.0,
 'KARACHI': 0.0008739684171712054,
 'KATHMANDU': 0.0053995028761682695,
 'KUALALUMPUR': 0.0230463826304979,
 'KUNMING': 0.0008153491788928612,
 'KUWAIT': 0.017111543637125557,
 'LAHORE': 0.0,
 'LEIPZIG': 0.0013656420006203,
 'LONDON': 0.009659255759431377,
 'LUXEMBOURG': 0.001632301097863998,
 'MALE': 0.008050867387540067,
 'MALPENSA': 0.0008739684171712054,
 'MANDALAY': 0.0,
 'MARUITIUS': 0.0021635560689517662,
 'MEDINA': 0.0023743033227257696,
 'MELBOURNE': 0.0,
 'MOSCOW': 0.00222031483194991,
 'MUENCHEN': 0.0008739684171712054,
 'MUSCAT': 0.021573147581820256,
 'NAIROBI': 0.0,
 'NARITA': 0.0008739684171712054,
 'NAVOI': 0.0008739684171712054,
 'NEW YORK': 0.0008739684171712054,
 'NEWARK': 0.001783398615868211,
 'OSAKA': 0.0023302731018383068,
 'PARIS': 0.0021635560689517662,
 'PARO': 0.0008153491788928612,
 'PEKING': 0.0,
 'RANGOON': 0.00737961582094142,
 'RIYADH': 0.010904317883093827,
 'ROME': 0.0,
 'SALALAH': 0.00014461954104718673,
 'SANAA': 0.0,
 'SEOUL': 0.001632301097863998,
 'SEYCHELLES': 0.0,
 'SHANGHAI': 0.0008739684171712054,
 'SHARJAH': 0.06798891203995014,
 'COIMBATORE': 4.562235327414671e-05,
 'NAGPUR': 4.467972333763876e-05,
 'SHENZHEN': 0.0,
 'SINGAPORE': 0.029148375732167088,
 'SYDNEY': 0.0,
 'TAIPAE': 0.0008739684171712054,
 'TASHKENT': 0.0005962909700470743,
 'TEHRAN': 0.0008739684171712054,
 'TELAVIV': 0.0,
 'TOKYO': 0.0,
 'TORONTO': 0.0,
 'VIENNA': 0.0,
 'ZURICH': 0.0008739684171712054,
 'BAGHDAD': 0.0,
 'REUNION': 0.0,
 'DON MUEANG': 0.0033248474317976507,
 'KANDAHAR': 0.0,
 'MANCHESTER': 0.0,
 'SAN FRANCISCO': 0.00029390815801190326,
 'BASRA': 0.0,
 'RASALKHAMA': 3.736451353236035e-05,
 'ANTALYA': 0.0008739684171712054,
 'BAKU': 0.0,
 'CHANDIGARH': 3.64171413324531e-05,
 'MADRID': 0.0,
 'MASHHAD': 0.0,
 'TANGERANG': 0.0,
 'VANCOUVER': 0.0}

nx.average_shortest_path_length(MDG)

2.517536103743

nx.pagerank_numpy(DG12)

{'ABUDHABI': 0.005356186395286574,
 'AHMEDABAD': 0.006921278899487179,
 'BENGALURU': 0.014851361101881422,
 'CHENNAI': 0.0209810611711548,
 'DELHI': 0.18290587292763807,
 'GOA': 0.01040426442265997,
 'HYDERABAD': 0.011108358915373494,
 'JAIPUR': 0.005796076242842705,
 'KOCHI': 0.0172855952488775,
 'KOLKATA': 0.023212289915499125,
 'KOZHIKODE': 0.02026175728725434,
 'LUCKNOW': 0.006575959195138676,
 'MANGALORE': 0.0063334797328987325,
 'MUMBAI': 0.09412860436881458,
 'PUNE': 0.005593982611002523,
 'TRIVANDRUM': 0.01655745893134999,
 'ADDIS ABABA': 0.005356186395286556,
 'ADEN': 0.005356186395286556,
 'AL AIN': 0.005356186395286556,
 'ALMATY': 0.005356186395286556,
 'AMSTERDAM': 0.005356186395286556,
 'ASHGABAT': 0.005356186395286556,
 'AMRITSAR': 0.009734390585959861,
 'BAHRAIN': 0.005356186395286556,
 'BANGKOK': 0.005356186395286556,
 'GAYA': 0.006304288926258301,
 'VARANASI': 0.007799555596336968,
 'BIRMINGHAM': 0.005356186395286567,
 'BISHKEK': 0.005356186395286567,
 'BRUSELLS': 0.005356186395286567,
 'CAIRO': 0.005356186395286556,
 'CHICAGO': 0.005356186395286556,
 'CHITTAGONG': 0.005356186395286556,
 'COLOGNE': 0.005356186395286556,
 'COLOMBO': 0.005356186395286556,
 'MADURAI': 0.0057843235218669855,
 'TIRUCHIRAPALLY': 0.007363365626639479,
 'DAMMAM': 0.005356186395286556,
 'DHAKA': 0.005356186395286556,
 'DOHA': 0.005356186395286556,
 'DUBAI': 0.005356186395286556,
 'VISAKHAPATNAM': 0.0054866216233896365,
 'DUSHANBE': 0.005356186395286567,
 'FRANKFURT': 0.005356186395286567,
 'GUANGZHOU': 0.005356186395286567,
 'HAA DHAALU ATOLL': 0.005356186395286556,
 'HELNSKI': 0.005356186395286556,
 'HO CHI MINH CITY': 0.005356186395286556,
 'HONG KONG': 0.005356186395286556,
 'ISTANBUL': 0.005356186395286556,
 'JEDDAH': 0.005356186395286556,
 'JOHANNESBERG': 0.005356186395286556,
 'KABUL': 0.005356186395286556,
 'KARACHI': 0.005356186395286556,
 'KATHMANDU': 0.005356186395286556,
 'KUALALUMPUR': 0.005356186395286556,
 'KUNMING': 0.005356186395286556,
 'KUWAIT': 0.005356186395286567,
 'LAHORE': 0.005356186395286567,
 'LEIPZIG': 0.005356186395286567,
 'LONDON': 0.005356186395286556,
 'LUXEMBOURG': 0.005356186395286556,
 'MALE': 0.005356186395286556,
 'MALPENSA': 0.005356186395286556,
 'MANDALAY': 0.005356186395286556,
 'MARUITIUS': 0.005356186395286556,
 'MEDINA': 0.005356186395286556,
 'MELBOURNE': 0.005356186395286556,
 'MOSCOW': 0.005356186395286556,
 'MUENCHEN': 0.005356186395286556,
 'MUSCAT': 0.005356186395286556,
 'NAIROBI': 0.005356186395286556,
 'NARITA': 0.005356186395286567,
 'NAVOI': 0.005356186395286567,
 'NEW YORK': 0.005356186395286567,
 'NEWARK': 0.005356186395286556,
 'OSAKA': 0.005356186395286556,
 'PARIS': 0.005356186395286556,
 'PARO': 0.005356186395286556,
 'PEKING': 0.005356186395286556,
 'RANGOON': 0.005356186395286556,
 'RIYADH': 0.005356186395286556,
 'ROME': 0.005356186395286556,
 'SALALAH': 0.005356186395286556,
 'SANAA': 0.005356186395286556,
 'SEOUL': 0.005356186395286556,
 'SEYCHELLES': 0.005356186395286556,
 'SHANGHAI': 0.005356186395286567,
 'SHARJAH': 0.005356186395286567,
 'COIMBATORE': 0.005576799736499401,
 'NAGPUR': 0.005538976975220321,
 'SHENZHEN': 0.005356186395286556,
 'SINGAPORE': 0.005356186395286556,
 'SYDNEY': 0.005356186395286556,
 'TAIPAE': 0.005356186395286556,
 'TASHKENT': 0.005356186395286556,
 'TEHRAN': 0.005356186395286556,
 'TELAVIV': 0.005356186395286556,
 'TOKYO': 0.005356186395286556,
 'TORONTO': 0.005356186395286556,
 'VIENNA': 0.005356186395286556,
 'ZURICH': 0.005356186395286567,
 'BAGHDAD': 0.005356186395286567,
 'REUNION': 0.005356186395286556,
 'DON MUEANG': 0.005356186395286556,
 'KANDAHAR': 0.005356186395286556,
 'MANCHESTER': 0.005356186395286556,
 'SAN FRANCISCO': 0.005356186395286556,
 'BASRA': 0.005356186395286556,
 'RASALKHAMA': 0.005356186395286556,
 'ANTALYA': 0.005356186395286556,
 'BAKU': 0.005356186395286556,
 'CHANDIGARH': 0.005368941674305577,
 'MADRID': 0.005356186395286554,
 'MASHHAD': 0.005356186395286554,
 'TANGERANG': 0.005356186395286565,
 'VANCOUVER': 0.005356186395286565}

nx.pagerank_numpy(DG21)

{'AHMEDABAD': 0.007278020378457034,
 'ABUDHABI': 0.018999038376819943,
 'BENGALURU': 0.00727802037845704,
 'CHENNAI': 0.0072780203784570535,
 'DELHI': 0.0072780203784570535,
 'GOA': 0.0072780203784570535,
 'HYDERABAD': 0.0072780203784570535,
 'JAIPUR': 0.0072780203784570535,
 'KOCHI': 0.0072780203784570535,
 'KOLKATA': 0.0072780203784570535,
 'KOZHIKODE': 0.0072780203784570535,
 'LUCKNOW': 0.0072780203784570535,
 'MANGALORE': 0.0072780203784570665,
 'MUMBAI': 0.0072780203784570665,
 'PUNE': 0.0072780203784570665,
 'TRIVANDRUM': 0.0072780203784570535,
 'ADDIS ABABA': 0.007456435300524049,
 'ADEN': 0.007278633048072945,
 'AL AIN': 0.007383921896278516,
 'ALMATY': 0.007302753595363129,
 'AMSTERDAM': 0.007504690777613966,
 'AMRITSAR': 0.0072780203784570535,
 'ASHGABAT': 0.007903437262073702,
 'BAHRAIN': 0.008778077029755107,
 'BANGKOK': 0.0108033850838755,
 'GAYA': 0.0072780203784570535,
 'VARANASI': 0.0072780203784570535,
 'BIRMINGHAM': 0.007859853218026205,
 'BISHKEK': 0.007287901096108702,
 'BRUSELLS': 0.0072780203784570665,
 'CAIRO': 0.007295607600371942,
 'CHICAGO': 0.007433075320400362,
 'CHITTAGONG': 0.007569772534991975,
 'COLOGNE': 0.0072780203784570535,
 'COLOMBO': 0.01398622211873123,
 'MADURAI': 0.0072780203784570535,
 'TIRUCHIRAPALLY': 0.0072780203784570535,
 'DAMMAM': 0.008792811110051237,
 'DHAKA': 0.00864362209307837,
 'DOHA': 0.01845632199221247,
 'DUBAI': 0.04153438309358315,
 'VISAKHAPATNAM': 0.0072780203784570535,
 'DUSHANBE': 0.007280778599992325,
 'FRANKFURT': 0.008761786944697765,
 'GUANGZHOU': 0.00736928259707759,
 'HAA DHAALU ATOLL': 0.007290780736933938,
 'HELNSKI': 0.007310535969796165,
 'HO CHI MINH CITY': 0.0072780203784570535,
 'HONG KONG': 0.00866531151908105,
 'ISTANBUL': 0.007501885029622345,
 'JEDDAH': 0.009886024922914755,
 'JOHANNESBERG': 0.007325736529717919,
 'KABUL': 0.007369153414549976,
 'KARACHI': 0.007293642891574412,
 'KATHMANDU': 0.00775872312838748,
 'KUALALUMPUR': 0.015166616023906414,
 'KUNMING': 0.007489544975598415,
 'KUWAIT': 0.009390077135850041,
 'LAHORE': 0.007285149857412772,
 'LEIPZIG': 0.0072780203784570665,
 'LONDON': 0.009411267576078054,
 'LUXEMBOURG': 0.0072780203784570535,
 'MALE': 0.007786976869171783,
 'MALPENSA': 0.007305951735776166,
 'MANDALAY': 0.0072780203784570535,
 'MARUITIUS': 0.00741395571601337,
 'MEDINA': 0.00729250430872075,
 'MELBOURNE': 0.007308664568855781,
 'MOSCOW': 0.007420482319139767,
 'MUENCHEN': 0.007418622437527349,
 'MUSCAT': 0.015531800996496729,
 'NAIROBI': 0.0073281431600391465,
 'NARITA': 0.007389201687874727,
 'NAVOI': 0.0072780203784570665,
 'NEW YORK': 0.0073743724067308995,
 'NEWARK': 0.007844862207392785,
 'OSAKA': 0.007283251037970271,
 'PARIS': 0.007705754844635763,
 'PARO': 0.0075112678923908655,
 'PEKING': 0.007381809810835203,
 'RANGOON': 0.014234794008601975,
 'RIYADH': 0.008927976939678742,
 'ROME': 0.007319058525198158,
 'SALALAH': 0.007358208876353794,
 'SANAA': 0.007280471056920608,
 'SEOUL': 0.0073427661051847955,
 'SEYCHELLES': 0.007305678607456617,
 'SHANGHAI': 0.007326727898209921,
 'SHARJAH': 0.02892123763858783,
 'COIMBATORE': 0.0072780203784570535,
 'NAGPUR': 0.0072780203784570535,
 'SHENZHEN': 0.0072780203784570535,
 'SINGAPORE': 0.01811113058528031,
 'SYDNEY': 0.007314840890242979,
 'TAIPAE': 0.00730476116167044,
 'TASHKENT': 0.007474201471853013,
 'TEHRAN': 0.0073103885006781414,
 'TELAVIV': 0.0073058628087790374,
 'TOKYO': 0.00732442832864276,
 'TORONTO': 0.007379543879472693,
 'VIENNA': 0.007306911876184012,
 'ZURICH': 0.007430811793251802,
 'BAGHDAD': 0.0072780203784570665,
 'REUNION': 0.007283006895534647,
 'DON MUEANG': 0.00879471441341036,
 'KANDAHAR': 0.007281613049292231,
 'MANCHESTER': 0.0072780203784570535,
 'SAN FRANCISCO': 0.007315184189959046,
 'BASRA': 0.007278781507944003,
 'RASALKHAMA': 0.007370705501842666,
 'ANTALYA': 0.007278781507944003,
 'BAKU': 0.0083668709743622,
 'CHANDIGARH': 0.0072780203784570535,
 'MADRID': 0.007283442553196629,
 'MASHHAD': 0.007280202515747612,
 'TANGERANG': 0.007295583574112502,
 'VANCOUVER': 0.007314757096171021}